{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Conversational_Chatbot.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/parulnith/Building-a-Conversational-Chatbot-for-Slack-using-Rasa-and-Python/blob/master/Conversational_Chatbot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "3oxVq6vQ76r7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Building a Conversational Chatbot for Slack using Rasa and Python -Part 1"
      ]
    },
    {
      "metadata": {
        "id": "kIFYA-Kp8aK4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Starting Jupyter Notebook with necessary imports"
      ]
    },
    {
      "metadata": {
        "id": "R0_7gOmu0r3v",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import logging, io, json, warnings\n",
        "logging.basicConfig(level=\"INFO\")\n",
        "warnings.filterwarnings('ignore')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IVAF41hr8jU5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Installations\n",
        "* Rasa NLU\n",
        "* Rasa Core\n",
        "* SpaCy Language Model"
      ]
    },
    {
      "metadata": {
        "id": "UsvAOHF_1dAY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 4049
        },
        "outputId": "f65c2c83-e7ae-46ef-e800-e43dcb854766"
      },
      "cell_type": "code",
      "source": [
        "import sys\n",
        "python = sys.executable\n",
        "\n",
        "# In your environment run:\n",
        "!{python} -m pip install -U rasa_core==0.9.6 rasa_nlu[spacy];"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting rasa_core==0.9.6\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/72/de/82e762685a991351a9f89d934b1b25fe04d10462cf4017f93227cb4d9058/rasa_core-0.9.6-py2.py3-none-any.whl (125kB)\n",
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            "\u001b[?25hCollecting rasa_nlu[spacy]\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a5/d2/2e6a081f3d222df01a3d941d7029e52c20619d53d557e721f096962d7293/rasa_nlu-0.13.8-py2.py3-none-any.whl (145kB)\n",
            "\u001b[K    100% |████████████████████████████████| 153kB 13.5MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: h5py~=2.0 in /usr/local/lib/python3.6/dist-packages (from rasa_core==0.9.6) (2.8.0)\n",
            "Collecting python-telegram-bot~=10.0 (from rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f1/51/d1bd383522c12b313eddd7b97b8e7d6cd2a8e3b44b8ff3c88e4a7b045cc8/python_telegram_bot-10.1.0-py2.py3-none-any.whl (298kB)\n",
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            "\u001b[?25hCollecting redis~=2.0 (from rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3b/f6/7a76333cf0b9251ecf49efff635015171843d9b977e4ffcf59f9c4428052/redis-2.10.6-py2.py3-none-any.whl (64kB)\n",
            "\u001b[K    100% |████████████████████████████████| 71kB 25.0MB/s \n",
            "\u001b[?25hCollecting tensorflow<1.9,>=1.7 (from rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/22/c6/d08f7c549330c2acc1b18b5c1f0f8d9d2af92f54d56861f331f372731671/tensorflow-1.8.0-cp36-cp36m-manylinux1_x86_64.whl (49.1MB)\n",
            "\u001b[K    100% |████████████████████████████████| 49.1MB 666kB/s \n",
            "\u001b[?25hCollecting ruamel.yaml~=0.15.0 (from rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/34/f0/483df7eba81448d377ee3130e249384f7a6a2ac3bc308e73378fc3d76223/ruamel.yaml-0.15.84-cp36-cp36m-manylinux1_x86_64.whl (663kB)\n",
            "\u001b[K    100% |████████████████████████████████| 665kB 16.5MB/s \n",
            "\u001b[?25hCollecting mattermostwrapper~=2.0 (from rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/93/70/203660597d12788e958dd691aa11c3c29caa075eadb2ce94d2eb53099d1b/mattermostwrapper-2.1-py2.py3-none-any.whl\n",
            "Collecting apscheduler~=3.0 (from rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/97/3a/fa3213cc325091b7729616594611fff31d72c2d4d590418c3efdf7424ae2/APScheduler-3.5.3-py2.py3-none-any.whl (57kB)\n",
            "\u001b[K    100% |████████████████████████████████| 61kB 23.5MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: keras~=2.0 in /usr/local/lib/python3.6/dist-packages (from rasa_core==0.9.6) (2.2.4)\n",
            "Requirement already satisfied, skipping upgrade: future~=0.16 in /usr/local/lib/python3.6/dist-packages (from rasa_core==0.9.6) (0.16.0)\n",
            "Collecting graphviz~=0.8.0 (from rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/53/39/4ab213673844e0c004bed8a0781a0721a3f6bb23eb8854ee75c236428892/graphviz-0.8.4-py2.py3-none-any.whl\n",
            "Collecting ConfigArgParse~=0.13.0 (from rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/77/61/ae928ce6ab85d4479ea198488cf5ffa371bd4ece2030c0ee85ff668deac5/ConfigArgParse-0.13.0.tar.gz\n",
            "Collecting pykwalify<=1.6.0 (from rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/6e/59/55c32d59b462a9c3fae2ab25f179f91d61617c0215e9f8ba3f4966b2b8b1/pykwalify-1.6.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: six~=1.0 in /usr/local/lib/python3.6/dist-packages (from rasa_core==0.9.6) (1.11.0)\n",
            "Collecting colorhash~=1.0 (from rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/0e/e1/50dbc513aa74e99eca4c47f2a8206711f0bec436fdddd95eebaf7eaaa1aa/colorhash-1.0.2-py2.py3-none-any.whl\n",
            "Collecting flask-cors~=3.0 (from rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/65/cb/683f71ff8daa3aea0a5cbb276074de39f9ab66d3fbb8ad5efb5bb83e90d2/Flask_Cors-3.0.7-py2.py3-none-any.whl\n",
            "Collecting typing~=3.0 (from rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/4a/bd/eee1157fc2d8514970b345d69cb9975dcd1e42cd7e61146ed841f6e68309/typing-3.6.6-py3-none-any.whl\n",
            "Collecting fbmessenger~=5.0 (from rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/1d/c2/3f74cbd68fcbb9680c4bf6434f3c08c9d90a0ddd1e94b954db7382264ef7/fbmessenger-5.3.2-py2.py3-none-any.whl\n",
            "Collecting fakeredis~=0.10.0 (from rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/64/bd/2756ddf350c4bb308e3255f9dcd6610f8b01344947bf74d5d166dc66b0a2/fakeredis-0.10.3-py2.py3-none-any.whl\n",
            "Collecting twilio~=6.0 (from rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/21/2e/91af5c8e20400792e62c19bacc197c4b7b8a7fbef1cac5ec9c73944facff/twilio-6.22.1-py2.py3-none-any.whl (986kB)\n",
            "\u001b[K    100% |████████████████████████████████| 993kB 15.5MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy~=1.13 in /usr/local/lib/python3.6/dist-packages (from rasa_core==0.9.6) (1.14.6)\n",
            "Requirement already satisfied, skipping upgrade: requests~=2.15 in /usr/local/lib/python3.6/dist-packages (from rasa_core==0.9.6) (2.18.4)\n",
            "Requirement already satisfied, skipping upgrade: tqdm~=4.0 in /usr/local/lib/python3.6/dist-packages (from rasa_core==0.9.6) (4.28.1)\n",
            "Requirement already satisfied, skipping upgrade: flask~=1.0 in /usr/local/lib/python3.6/dist-packages (from rasa_core==0.9.6) (1.0.2)\n",
            "Requirement already satisfied, skipping upgrade: networkx~=2.0 in /usr/local/lib/python3.6/dist-packages (from rasa_core==0.9.6) (2.2)\n",
            "Collecting slackclient~=1.0 (from rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/0d/2f/1378e64a843a5a8a83d73caa59ac88c36c67e2b41ac0fab3422080ff13bd/slackclient-1.3.0-py2.py3-none-any.whl\n",
            "Collecting jsonpickle~=0.9.0 (from rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/32/d5/2f47f03d3f64c31b0d7070b488274631d7567c36e81a9f744e6638bb0f0d/jsonpickle-0.9.6.tar.gz (67kB)\n",
            "\u001b[K    100% |████████████████████████████████| 71kB 23.8MB/s \n",
            "\u001b[?25hCollecting coloredlogs~=10.0 (from rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/0f/7877fc42fff0b9d70b6442df62d53b3868d3a6ad1b876bdb54335b30ff23/coloredlogs-10.0-py2.py3-none-any.whl (47kB)\n",
            "\u001b[K    100% |████████████████████████████████| 51kB 21.5MB/s \n",
            "\u001b[?25hCollecting scikit-learn~=0.19.0 (from rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f9/c8/8db4108aba5e2166cd2ea4eafa1a4b82f89240a1fa85733029cc2358ad1f/scikit_learn-0.19.2-cp36-cp36m-manylinux1_x86_64.whl (4.9MB)\n",
            "\u001b[K    100% |████████████████████████████████| 4.9MB 6.6MB/s \n",
            "\u001b[?25hCollecting klein (from rasa_nlu[spacy])\n",
            "  Downloading https://files.pythonhosted.org/packages/8a/6b/adc97a7bb3fb781fdd9e49177ad873c1479f87b9745271cbeda81cbb9cc8/klein-17.10.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: matplotlib~=2.0 in /usr/local/lib/python3.6/dist-packages (from rasa_nlu[spacy]) (2.1.2)\n",
            "Requirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.6/dist-packages (from rasa_nlu[spacy]) (18.0)\n",
            "Requirement already satisfied, skipping upgrade: pathlib in /usr/local/lib/python3.6/dist-packages (from rasa_nlu[spacy]) (1.0.1)\n",
            "Collecting simplejson (from rasa_nlu[spacy])\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e3/24/c35fb1c1c315fc0fffe61ea00d3f88e85469004713dab488dee4f35b0aff/simplejson-3.16.0.tar.gz (81kB)\n",
            "\u001b[K    100% |████████████████████████████████| 81kB 22.7MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: gevent in /usr/local/lib/python3.6/dist-packages (from rasa_nlu[spacy]) (1.3.7)\n",
            "Requirement already satisfied, skipping upgrade: pyyaml in /usr/local/lib/python3.6/dist-packages (from rasa_nlu[spacy]) (3.13)\n",
            "Requirement already satisfied, skipping upgrade: cloudpickle in /usr/local/lib/python3.6/dist-packages (from rasa_nlu[spacy]) (0.6.1)\n",
            "Requirement already satisfied, skipping upgrade: boto3 in /usr/local/lib/python3.6/dist-packages (from rasa_nlu[spacy]) (1.9.72)\n",
            "Requirement already satisfied, skipping upgrade: jsonschema in /usr/local/lib/python3.6/dist-packages (from rasa_nlu[spacy]) (2.6.0)\n",
            "Collecting sklearn-crfsuite; extra == \"spacy\" (from rasa_nlu[spacy])\n",
            "  Downloading https://files.pythonhosted.org/packages/25/74/5b7befa513482e6dee1f3dd68171a6c9dfc14c0eaa00f885ffeba54fe9b0/sklearn_crfsuite-0.3.6-py2.py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: scipy; extra == \"spacy\" in /usr/local/lib/python3.6/dist-packages (from rasa_nlu[spacy]) (1.1.0)\n",
            "Collecting spacy<=2.0.12,>2.0; extra == \"spacy\" (from rasa_nlu[spacy])\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/24/de/ac14cd453c98656d6738a5669f96a4ac7f668493d5e6b78227ac933c5fd4/spacy-2.0.12.tar.gz (22.0MB)\n",
            "\u001b[K    100% |████████████████████████████████| 22.0MB 1.5MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: certifi in /usr/local/lib/python3.6/dist-packages (from python-telegram-bot~=10.0->rasa_core==0.9.6) (2018.11.29)\n",
            "Collecting tensorboard<1.9.0,>=1.8.0 (from tensorflow<1.9,>=1.7->rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/59/a6/0ae6092b7542cfedba6b2a1c9b8dceaf278238c39484f3ba03b03f07803c/tensorboard-1.8.0-py3-none-any.whl (3.1MB)\n",
            "\u001b[K    100% |████████████████████████████████| 3.1MB 10.4MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (0.7.1)\n",
            "Requirement already satisfied, skipping upgrade: protobuf>=3.4.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (3.6.1)\n",
            "Requirement already satisfied, skipping upgrade: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (0.32.3)\n",
            "Requirement already satisfied, skipping upgrade: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (0.6.1)\n",
            "Requirement already satisfied, skipping upgrade: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<1.9,>=1.7->rasa_core==0.9.6) (0.2.0)\n",
            "Collecting tzlocal>=1.2 (from apscheduler~=3.0->rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/cb/89/e3687d3ed99bc882793f82634e9824e62499fdfdc4b1ae39e211c5b05017/tzlocal-1.5.1.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: pytz in /usr/local/lib/python3.6/dist-packages (from apscheduler~=3.0->rasa_core==0.9.6) (2018.7)\n",
            "Requirement already satisfied, skipping upgrade: setuptools>=0.7 in /usr/local/lib/python3.6/dist-packages (from apscheduler~=3.0->rasa_core==0.9.6) (40.6.3)\n",
            "Requirement already satisfied, skipping upgrade: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras~=2.0->rasa_core==0.9.6) (1.0.5)\n",
            "Requirement already satisfied, skipping upgrade: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras~=2.0->rasa_core==0.9.6) (1.0.6)\n",
            "Requirement already satisfied, skipping upgrade: docopt>=0.6.2 in /usr/local/lib/python3.6/dist-packages (from pykwalify<=1.6.0->rasa_core==0.9.6) (0.6.2)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.4.2 in /usr/local/lib/python3.6/dist-packages (from pykwalify<=1.6.0->rasa_core==0.9.6) (2.5.3)\n",
            "Collecting PyJWT>=1.4.2 (from twilio~=6.0->rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/87/8b/6a9f14b5f781697e51259d81657e6048fd31a113229cf346880bb7545565/PyJWT-1.7.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: pysocks; python_version >= \"3.0\" in /usr/local/lib/python3.6/dist-packages (from twilio~=6.0->rasa_core==0.9.6) (1.6.8)\n",
            "Requirement already satisfied, skipping upgrade: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests~=2.15->rasa_core==0.9.6) (1.22)\n",
            "Requirement already satisfied, skipping upgrade: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests~=2.15->rasa_core==0.9.6) (2.6)\n",
            "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests~=2.15->rasa_core==0.9.6) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: itsdangerous>=0.24 in /usr/local/lib/python3.6/dist-packages (from flask~=1.0->rasa_core==0.9.6) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: click>=5.1 in /usr/local/lib/python3.6/dist-packages (from flask~=1.0->rasa_core==0.9.6) (7.0)\n",
            "Requirement already satisfied, skipping upgrade: Jinja2>=2.10 in /usr/local/lib/python3.6/dist-packages (from flask~=1.0->rasa_core==0.9.6) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: Werkzeug>=0.14 in /usr/local/lib/python3.6/dist-packages (from flask~=1.0->rasa_core==0.9.6) (0.14.1)\n",
            "Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx~=2.0->rasa_core==0.9.6) (4.3.0)\n",
            "Collecting websocket-client<1.0a0,>=0.35 (from slackclient~=1.0->rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/26/2d/f749a5c82f6192d77ed061a38e02001afcba55fe8477336d26a950ab17ce/websocket_client-0.54.0-py2.py3-none-any.whl (200kB)\n",
            "\u001b[K    100% |████████████████████████████████| 204kB 27.8MB/s \n",
            "\u001b[?25hCollecting humanfriendly>=4.7 (from coloredlogs~=10.0->rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/79/1e/13d96248e3fcaa7777b61fa889feab44865c85e524bbd667acfa0d8b66e3/humanfriendly-4.17-py2.py3-none-any.whl (72kB)\n",
            "\u001b[K    100% |████████████████████████████████| 81kB 24.1MB/s \n",
            "\u001b[?25hCollecting Twisted>=15.5 (from klein->rasa_nlu[spacy])\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5d/0e/a72d85a55761c2c3ff1cb968143a2fd5f360220779ed90e0fadf4106d4f2/Twisted-18.9.0.tar.bz2 (3.1MB)\n",
            "\u001b[K    100% |████████████████████████████████| 3.1MB 10.5MB/s \n",
            "\u001b[?25hCollecting incremental (from klein->rasa_nlu[spacy])\n",
            "  Downloading https://files.pythonhosted.org/packages/f5/1d/c98a587dc06e107115cf4a58b49de20b19222c83d75335a192052af4c4b7/incremental-17.5.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib~=2.0->rasa_nlu[spacy]) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib~=2.0->rasa_nlu[spacy]) (2.3.0)\n",
            "Requirement already satisfied, skipping upgrade: greenlet>=0.4.14; platform_python_implementation == \"CPython\" in /usr/local/lib/python3.6/dist-packages (from gevent->rasa_nlu[spacy]) (0.4.15)\n",
            "Requirement already satisfied, skipping upgrade: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->rasa_nlu[spacy]) (0.9.3)\n",
            "Requirement already satisfied, skipping upgrade: botocore<1.13.0,>=1.12.72 in /usr/local/lib/python3.6/dist-packages (from boto3->rasa_nlu[spacy]) (1.12.72)\n",
            "Requirement already satisfied, skipping upgrade: s3transfer<0.2.0,>=0.1.10 in /usr/local/lib/python3.6/dist-packages (from boto3->rasa_nlu[spacy]) (0.1.13)\n",
            "Collecting python-crfsuite>=0.8.3 (from sklearn-crfsuite; extra == \"spacy\"->rasa_nlu[spacy])\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2f/86/cfcd71edca9d25d3d331209a20f6314b6f3f134c29478f90559cee9ce091/python_crfsuite-0.9.6-cp36-cp36m-manylinux1_x86_64.whl (754kB)\n",
            "\u001b[K    100% |████████████████████████████████| 757kB 18.8MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: tabulate in /usr/local/lib/python3.6/dist-packages (from sklearn-crfsuite; extra == \"spacy\"->rasa_nlu[spacy]) (0.8.2)\n",
            "Collecting murmurhash<0.29,>=0.28 (from spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy])\n",
            "  Downloading https://files.pythonhosted.org/packages/82/55/7f050e9f73c9a58df219c63e77304b0ff01676847061dc99abb484cff3a8/murmurhash-0.28.0-cp36-cp36m-manylinux1_x86_64.whl\n",
            "Collecting cymem<1.32,>=1.30 (from spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy])\n",
            "  Downloading https://files.pythonhosted.org/packages/a5/0f/d29aa68c55db37844c77e7e96143bd96651fd0f4453c9f6ee043ac846b77/cymem-1.31.2-cp36-cp36m-manylinux1_x86_64.whl\n",
            "Collecting preshed<2.0.0,>=1.0.0 (from spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy])\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/12/88/57a818051f3d71e800bfb7ba4df56d3ea5793482ef11f1d2109b726f3bac/preshed-1.0.1-cp36-cp36m-manylinux1_x86_64.whl (80kB)\n",
            "\u001b[K    100% |████████████████████████████████| 81kB 27.9MB/s \n",
            "\u001b[?25hCollecting thinc<6.11.0,>=6.10.3 (from spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy])\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/94/b1/47a88072d0a38b3594c0a638a62f9ef7c742b8b8a87f7b105f7ed720b14b/thinc-6.10.3.tar.gz (1.2MB)\n",
            "\u001b[K    100% |████████████████████████████████| 1.2MB 16.8MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: plac<1.0.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.9.6)\n",
            "Requirement already satisfied, skipping upgrade: ujson>=1.35 in /usr/local/lib/python3.6/dist-packages (from spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (1.35)\n",
            "Requirement already satisfied, skipping upgrade: dill<0.3,>=0.2 in /usr/local/lib/python3.6/dist-packages (from spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.2.8.2)\n",
            "Collecting regex==2017.4.5 (from spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy])\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/36/62/c0c0d762ffd4ffaf39f372eb8561b8d491a11ace5a7884610424a8b40f95/regex-2017.04.05.tar.gz (601kB)\n",
            "\u001b[K    100% |████████████████████████████████| 604kB 16.0MB/s \n",
            "\u001b[?25hCollecting bleach==1.5.0 (from tensorboard<1.9.0,>=1.8.0->tensorflow<1.9,>=1.7->rasa_core==0.9.6)\n",
            "  Downloading https://files.pythonhosted.org/packages/33/70/86c5fec937ea4964184d4d6c4f0b9551564f821e1c3575907639036d9b90/bleach-1.5.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow<1.9,>=1.7->rasa_core==0.9.6) (3.0.1)\n",
            "Collecting html5lib==0.9999999 (from tensorboard<1.9.0,>=1.8.0->tensorflow<1.9,>=1.7->rasa_core==0.9.6)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/ae/bcb60402c60932b32dfaf19bb53870b29eda2cd17551ba5639219fb5ebf9/html5lib-0.9999999.tar.gz (889kB)\n",
            "\u001b[K    100% |████████████████████████████████| 890kB 15.2MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.10->flask~=1.0->rasa_core==0.9.6) (1.1.0)\n",
            "Collecting zope.interface>=4.4.2 (from Twisted>=15.5->klein->rasa_nlu[spacy])\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/19/17/1d198a6aaa9aa4590862fe3d3a2ed7dd808050cab4eebe8a2f2f813c1376/zope.interface-4.6.0-cp36-cp36m-manylinux1_x86_64.whl (167kB)\n",
            "\u001b[K    100% |████████████████████████████████| 174kB 27.6MB/s \n",
            "\u001b[?25hCollecting constantly>=15.1 (from Twisted>=15.5->klein->rasa_nlu[spacy])\n",
            "  Downloading https://files.pythonhosted.org/packages/b9/65/48c1909d0c0aeae6c10213340ce682db01b48ea900a7d9fce7a7910ff318/constantly-15.1.0-py2.py3-none-any.whl\n",
            "Collecting Automat>=0.3.0 (from Twisted>=15.5->klein->rasa_nlu[spacy])\n",
            "  Downloading https://files.pythonhosted.org/packages/a3/86/14c16bb98a5a3542ed8fed5d74fb064a902de3bdd98d6584b34553353c45/Automat-0.7.0-py2.py3-none-any.whl\n",
            "Collecting hyperlink>=17.1.1 (from Twisted>=15.5->klein->rasa_nlu[spacy])\n",
            "  Downloading https://files.pythonhosted.org/packages/a7/b6/84d0c863ff81e8e7de87cff3bd8fd8f1054c227ce09af1b679a8b17a9274/hyperlink-18.0.0-py2.py3-none-any.whl\n",
            "Collecting PyHamcrest>=1.9.0 (from Twisted>=15.5->klein->rasa_nlu[spacy])\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9a/d5/d37fd731b7d0e91afcc84577edeccf4638b4f9b82f5ffe2f8b62e2ddc609/PyHamcrest-1.9.0-py2.py3-none-any.whl (52kB)\n",
            "\u001b[K    100% |████████████████████████████████| 61kB 22.5MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from Twisted>=15.5->klein->rasa_nlu[spacy]) (18.2.0)\n",
            "Requirement already satisfied, skipping upgrade: docutils>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.13.0,>=1.12.72->boto3->rasa_nlu[spacy]) (0.14)\n",
            "Requirement already satisfied, skipping upgrade: msgpack<1.0.0,>=0.5.6 in /usr/local/lib/python3.6/dist-packages (from thinc<6.11.0,>=6.10.3->spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.5.6)\n",
            "Requirement already satisfied, skipping upgrade: msgpack-numpy<1.0.0,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from thinc<6.11.0,>=6.10.3->spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.4.3.2)\n",
            "Requirement already satisfied, skipping upgrade: cytoolz<0.10,>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.11.0,>=6.10.3->spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.9.0.1)\n",
            "Requirement already satisfied, skipping upgrade: wrapt<1.11.0,>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.11.0,>=6.10.3->spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (1.10.11)\n",
            "Requirement already satisfied, skipping upgrade: toolz>=0.8.0 in /usr/local/lib/python3.6/dist-packages (from cytoolz<0.10,>=0.9.0->thinc<6.11.0,>=6.10.3->spacy<=2.0.12,>2.0; extra == \"spacy\"->rasa_nlu[spacy]) (0.9.0)\n",
            "Building wheels for collected packages: ConfigArgParse, jsonpickle, simplejson, spacy, tzlocal, Twisted, thinc, regex, html5lib\n",
            "  Running setup.py bdist_wheel for ConfigArgParse ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/76/11/eb/12113becd46c0e8a70b8a63f9405e46a1f61d4aaa8532d676b\n",
            "  Running setup.py bdist_wheel for jsonpickle ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/07/8b/41/8ce98f4737a9ff61b1bf2673f2abfe66a6a43ad6e91d2c9736\n",
            "  Running setup.py bdist_wheel for simplejson ... \u001b[?25l-\b \b\\\b \b|\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/5d/1a/1e/0350bb3df3e74215cd91325344cc86c2c691f5306eb4d22c77\n",
            "  Running setup.py bdist_wheel for spacy ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/60/0b/bb/7c2e28db574dbb2358176934eddd32a1c5f838ba0bc23eaaab\n",
            "  Running setup.py bdist_wheel for tzlocal ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/15/ae/df/a67bf1ed84e9bf230187d36d8dcfd30072bea0236cb059ed91\n",
            "  Running setup.py bdist_wheel for Twisted ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/57/2e/89/11ba83bc08ac30a5e3a6005f0310c78d231b96a270def88ca0\n",
            "  Running setup.py bdist_wheel for thinc ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/db/bc/e1/9b321b6b203288cf636a56e668ed5700076af4ed66062278ca\n",
            "  Running setup.py bdist_wheel for regex ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/75/07/38/3c16b529d50cb4e0cd3dbc7b75cece8a09c132692c74450b01\n",
            "  Running setup.py bdist_wheel for html5lib ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/50/ae/f9/d2b189788efcf61d1ee0e36045476735c838898eef1cad6e29\n",
            "Successfully built ConfigArgParse jsonpickle simplejson spacy tzlocal Twisted thinc regex html5lib\n",
            "\u001b[31myellowbrick 0.9 has requirement scikit-learn>=0.20, but you'll have scikit-learn 0.19.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mmagenta 0.3.19 has requirement tensorflow>=1.12.0, but you'll have tensorflow 1.8.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mimbalanced-learn 0.4.3 has requirement scikit-learn>=0.20, but you'll have scikit-learn 0.19.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mrasa-core 0.9.6 has requirement rasa-nlu~=0.12.0, but you'll have rasa-nlu 0.13.8 which is incompatible.\u001b[0m\n",
            "Installing collected packages: python-telegram-bot, redis, html5lib, bleach, tensorboard, tensorflow, ruamel.yaml, mattermostwrapper, tzlocal, apscheduler, graphviz, ConfigArgParse, pykwalify, colorhash, flask-cors, typing, fbmessenger, fakeredis, PyJWT, twilio, websocket-client, slackclient, jsonpickle, zope.interface, constantly, incremental, Automat, hyperlink, PyHamcrest, Twisted, klein, humanfriendly, coloredlogs, simplejson, scikit-learn, python-crfsuite, sklearn-crfsuite, murmurhash, cymem, preshed, thinc, regex, spacy, rasa-nlu, rasa-core\n",
            "  Found existing installation: html5lib 1.0.1\n",
            "    Uninstalling html5lib-1.0.1:\n",
            "      Successfully uninstalled html5lib-1.0.1\n",
            "  Found existing installation: bleach 3.0.2\n",
            "    Uninstalling bleach-3.0.2:\n",
            "      Successfully uninstalled bleach-3.0.2\n",
            "  Found existing installation: tensorboard 1.12.2\n",
            "    Uninstalling tensorboard-1.12.2:\n",
            "      Successfully uninstalled tensorboard-1.12.2\n",
            "  Found existing installation: tensorflow 1.12.0\n",
            "    Uninstalling tensorflow-1.12.0:\n",
            "      Successfully uninstalled tensorflow-1.12.0\n",
            "  Found existing installation: graphviz 0.10.1\n",
            "    Uninstalling graphviz-0.10.1:\n",
            "      Successfully uninstalled graphviz-0.10.1\n",
            "  Found existing installation: scikit-learn 0.20.2\n",
            "    Uninstalling scikit-learn-0.20.2:\n",
            "      Successfully uninstalled scikit-learn-0.20.2\n",
            "  Found existing installation: murmurhash 1.0.1\n",
            "    Uninstalling murmurhash-1.0.1:\n",
            "      Successfully uninstalled murmurhash-1.0.1\n",
            "  Found existing installation: cymem 2.0.2\n",
            "    Uninstalling cymem-2.0.2:\n",
            "      Successfully uninstalled cymem-2.0.2\n",
            "  Found existing installation: preshed 2.0.1\n",
            "    Uninstalling preshed-2.0.1:\n",
            "      Successfully uninstalled preshed-2.0.1\n",
            "  Found existing installation: thinc 6.12.1\n",
            "    Uninstalling thinc-6.12.1:\n",
            "      Successfully uninstalled thinc-6.12.1\n",
            "  Found existing installation: regex 2018.1.10\n",
            "    Uninstalling regex-2018.1.10:\n",
            "      Successfully uninstalled regex-2018.1.10\n",
            "  Found existing installation: spacy 2.0.18\n",
            "    Uninstalling spacy-2.0.18:\n",
            "      Successfully uninstalled spacy-2.0.18\n",
            "Successfully installed Automat-0.7.0 ConfigArgParse-0.13.0 PyHamcrest-1.9.0 PyJWT-1.7.1 Twisted-18.9.0 apscheduler-3.5.3 bleach-1.5.0 coloredlogs-10.0 colorhash-1.0.2 constantly-15.1.0 cymem-1.31.2 fakeredis-0.10.3 fbmessenger-5.3.2 flask-cors-3.0.7 graphviz-0.8.4 html5lib-0.9999999 humanfriendly-4.17 hyperlink-18.0.0 incremental-17.5.0 jsonpickle-0.9.6 klein-17.10.0 mattermostwrapper-2.1 murmurhash-0.28.0 preshed-1.0.1 pykwalify-1.6.0 python-crfsuite-0.9.6 python-telegram-bot-10.1.0 rasa-core-0.9.6 rasa-nlu-0.13.8 redis-2.10.6 regex-2017.4.5 ruamel.yaml-0.15.84 scikit-learn-0.19.2 simplejson-3.16.0 sklearn-crfsuite-0.3.6 slackclient-1.3.0 spacy-2.0.12 tensorboard-1.8.0 tensorflow-1.8.0 thinc-6.10.3 twilio-6.22.1 typing-3.6.6 tzlocal-1.5.1 websocket-client-0.54.0 zope.interface-4.6.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wyCva14-1gD4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "642d04c1-b9ad-4ed0-fb28-ee9bef21507b"
      },
      "cell_type": "code",
      "source": [
        "!{python} -m spacy download en_core_web_md"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting en_core_web_md==2.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_md-2.0.0/en_core_web_md-2.0.0.tar.gz#egg=en_core_web_md==2.0.0\n",
            "\u001b[?25l  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_md-2.0.0/en_core_web_md-2.0.0.tar.gz (120.8MB)\n",
            "\u001b[K    100% |████████████████████████████████| 120.9MB 61.3MB/s \n",
            "\u001b[?25hInstalling collected packages: en-core-web-md\n",
            "  Running setup.py install for en-core-web-md ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \bdone\n",
            "\u001b[?25hSuccessfully installed en-core-web-md-2.0.0\n",
            "\n",
            "\u001b[93m    Linking successful\u001b[0m\n",
            "    /usr/local/lib/python3.6/dist-packages/en_core_web_md -->\n",
            "    /usr/local/lib/python3.6/dist-packages/spacy/data/en_core_web_md\n",
            "\n",
            "    You can now load the model via spacy.load('en_core_web_md')\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "v7JQlbqR9CHC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Downloading the English Language Model"
      ]
    },
    {
      "metadata": {
        "id": "eRmnEdML3OhH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "cb852307-d652-40c3-cf3d-66c54f833908"
      },
      "cell_type": "code",
      "source": [
        "!{python} -m spacy link en_core_web_md en --force;"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\u001b[93m    Linking successful\u001b[0m\n",
            "    /usr/local/lib/python3.6/dist-packages/en_core_web_md -->\n",
            "    /usr/local/lib/python3.6/dist-packages/spacy/data/en\n",
            "\n",
            "    You can now load the model via spacy.load('en')\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yHYeAA859JGq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Importing the Installations"
      ]
    },
    {
      "metadata": {
        "id": "TSw6zFmk3iPu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import rasa_nlu\n",
        "import rasa_core\n",
        "import spacy\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aEw5vhSq9gWa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 1. Teaching the bot to understand user inputs using Rasa NLU"
      ]
    },
    {
      "metadata": {
        "id": "oDEAOmaI9o4a",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Preparing the NLU Training Data"
      ]
    },
    {
      "metadata": {
        "id": "RPxeQ1_14CjK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bfb5974f-f8ea-46b8-b8cb-9dd4479ed9cc"
      },
      "cell_type": "code",
      "source": [
        "nlu_md = \"\"\"\n",
        "## intent:greet\n",
        "- hey\n",
        "- hello there\n",
        "- hi\n",
        "- hello there\n",
        "- good morning\n",
        "- good evening\n",
        "- moin\n",
        "- hey there\n",
        "- let's go\n",
        "- hey dude\n",
        "- goodmorning\n",
        "- goodevening\n",
        "- good afternoon\n",
        "\n",
        "## intent:goodbye\n",
        "- cu\n",
        "- good by\n",
        "- cee you later\n",
        "- good night\n",
        "- good afternoon\n",
        "- bye\n",
        "- goodbye\n",
        "- have a nice day\n",
        "- see you around\n",
        "- bye bye\n",
        "- see you later\n",
        "\n",
        "## intent:mood_affirm\n",
        "- yes\n",
        "- indeed\n",
        "- of course\n",
        "- that sounds good\n",
        "- correct\n",
        "\n",
        "## intent:mood_deny\n",
        "- no\n",
        "- never\n",
        "- I don't think so\n",
        "- don't like that\n",
        "- no way\n",
        "- not really\n",
        "\n",
        "## intent:mood_great\n",
        "- perfect\n",
        "- very good\n",
        "- great\n",
        "- amazing\n",
        "- feeling like a king\n",
        "- wonderful\n",
        "- I am feeling very good\n",
        "- I am great\n",
        "- I am amazing\n",
        "- I am going to save the world\n",
        "- super\n",
        "- extremely good\n",
        "- so so perfect\n",
        "- so good\n",
        "- so perfect\n",
        "\n",
        "## intent:mood_unhappy\n",
        "- my day was horrible\n",
        "- I am sad\n",
        "- I don't feel very well\n",
        "- I am disappointed\n",
        "- super sad\n",
        "- I'm so sad\n",
        "- sad\n",
        "- very sad\n",
        "- unhappy\n",
        "- bad\n",
        "- very bad\n",
        "- awful\n",
        "- terrible\n",
        "- not so good\n",
        "- not very good\n",
        "- extremly sad\n",
        "- so saad\n",
        "- Quite bad - can I get a cute picture of a [bird](group:birds), please?\n",
        "- Really bad and only [doggo](group:shibes) pics and change that.\n",
        "- Not good. The only thing that could make me fell better is a picture of a cute [kitten](group:cats).\n",
        "- so sad. Only the picture of a [puppy](group:shibes) could make it better.\n",
        "- I am very sad. I need a [cat](group:cats) picture.\n",
        "- Extremely sad. Only the cute [doggo](group:shibes) pics can make me feel better.\n",
        "- Bad. Please show me a [bird](group:birds) pic!\n",
        "- Pretty bad to be honest. Can you show me a [puppy](group:shibes) picture to make me fell better?\n",
        "\n",
        "## intent: inform\n",
        "- A [dog](group:shibes)\n",
        "- [dog](group:shibes)\n",
        "- [bird](group:birds)\n",
        "- a [cat](group:cats)\n",
        "- [cat](group:cats)\n",
        "- a [bird](group:birds)\n",
        "- of a [dog](group:shibes)\n",
        "- of a [cat](group:cats)\n",
        "- a [bird](group:birds), please\n",
        "- a [dog](group:shibes), please\n",
        "\"\"\"\n",
        "\n",
        "%store nlu_md > nlu.md"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing 'nlu_md' (str) to file 'nlu.md'.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ceazcacn9veB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Defining the NLU Model Configuration"
      ]
    },
    {
      "metadata": {
        "id": "dF60NWhR4ID6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "92946645-94fc-4450-aa41-eca8895ff83c"
      },
      "cell_type": "code",
      "source": [
        "config = \"\"\"\n",
        "language: \"en\"\n",
        "\n",
        "pipeline:\n",
        "- name: \"nlp_spacy\"                   # loads the spacy language model\n",
        "- name: \"tokenizer_spacy\"             # splits the sentence into tokens\n",
        "- name: \"ner_crf\"                   # uses the pretrained spacy NER model\n",
        "- name: \"intent_featurizer_spacy\"     # transform the sentence into a vector representation\n",
        "- name: \"intent_classifier_sklearn\"   # uses the vector representation to classify using SVM\n",
        "- name: \"ner_synonyms\"                # trains the synonyms\n",
        "\"\"\" \n",
        "\n",
        "%store config > config.yml"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing 'config' (str) to file 'config.yml'.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ieoWk91X9y8X",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training the NLU Model."
      ]
    },
    {
      "metadata": {
        "id": "dp3AIHmS4L6x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "8011c4f7-c789-4138-84d7-4710207615d8"
      },
      "cell_type": "code",
      "source": [
        "from rasa_nlu.training_data import load_data\n",
        "from rasa_nlu.config import RasaNLUModelConfig\n",
        "from rasa_nlu.model import Trainer\n",
        "from rasa_nlu import config\n",
        "\n",
        "# loading the nlu training samples\n",
        "training_data = load_data(\"nlu.md\")\n",
        "\n",
        "# trainer to educate our pipeline\n",
        "trainer = Trainer(config.load(\"config.yml\"))\n",
        "\n",
        "# train the model!\n",
        "interpreter = trainer.train(training_data)\n",
        "\n",
        "# store it for future use\n",
        "model_directory = trainer.persist(\"./models/nlu\", fixed_model_name=\"current\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:rasa_nlu.training_data.loading:Training data format of nlu.md is md\n",
            "INFO:rasa_nlu.training_data.training_data:Training data stats: \n",
            "\t- intent examples: 85 (7 distinct intents)\n",
            "\t- Found intents: 'greet', 'mood_deny', 'mood_great', 'mood_affirm', 'mood_unhappy', 'goodbye', 'inform'\n",
            "\t- entity examples: 18 (1 distinct entities)\n",
            "\t- found entities: 'group'\n",
            "\n",
            "INFO:rasa_nlu.utils.spacy_utils:Trying to load spacy model with name 'en'\n",
            "INFO:rasa_nlu.components:Added 'nlp_spacy' to component cache. Key 'nlp_spacy-en'.\n",
            "INFO:rasa_nlu.model:Starting to train component nlp_spacy\n",
            "INFO:rasa_nlu.model:Finished training component.\n",
            "INFO:rasa_nlu.model:Starting to train component tokenizer_spacy\n",
            "INFO:rasa_nlu.model:Finished training component.\n",
            "INFO:rasa_nlu.model:Starting to train component ner_crf\n",
            "INFO:rasa_nlu.model:Finished training component.\n",
            "INFO:rasa_nlu.model:Starting to train component intent_featurizer_spacy\n",
            "INFO:rasa_nlu.model:Finished training component.\n",
            "INFO:rasa_nlu.model:Starting to train component intent_classifier_sklearn\n",
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s finished\n",
            "INFO:rasa_nlu.model:Finished training component.\n",
            "INFO:rasa_nlu.model:Starting to train component ner_synonyms\n",
            "INFO:rasa_nlu.model:Finished training component.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fitting 2 folds for each of 6 candidates, totalling 12 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:rasa_nlu.model:Successfully saved model into '/content/models/nlu/default/current'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "Jrfp4xOS95ZZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Evaluating the NLU model on a random text"
      ]
    },
    {
      "metadata": {
        "id": "4UjzlqMV4N1k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "outputId": "37ea93e5-6a71-4e8e-d2b6-a45144d184ad"
      },
      "cell_type": "code",
      "source": [
        "# A helper function for prettier output\n",
        "\n",
        "def pprint(o):   \n",
        "    print(json.dumps(o, indent=2))\n",
        "    \n",
        "pprint(interpreter.parse(\"I am very sad. Could you send me a cat picture? \"))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"intent\": {\n",
            "    \"name\": \"mood_unhappy\",\n",
            "    \"confidence\": 0.7165062638624508\n",
            "  },\n",
            "  \"entities\": [\n",
            "    {\n",
            "      \"start\": 35,\n",
            "      \"end\": 38,\n",
            "      \"value\": \"cats\",\n",
            "      \"entity\": \"group\",\n",
            "      \"confidence\": 0.9543162015604525,\n",
            "      \"extractor\": \"ner_crf\",\n",
            "      \"processors\": [\n",
            "        \"ner_synonyms\"\n",
            "      ]\n",
            "    }\n",
            "  ],\n",
            "  \"intent_ranking\": [\n",
            "    {\n",
            "      \"name\": \"mood_unhappy\",\n",
            "      \"confidence\": 0.7165062638624508\n",
            "    },\n",
            "    {\n",
            "      \"name\": \"goodbye\",\n",
            "      \"confidence\": 0.07658387975889518\n",
            "    },\n",
            "    {\n",
            "      \"name\": \"mood_great\",\n",
            "      \"confidence\": 0.0747897961451117\n",
            "    },\n",
            "    {\n",
            "      \"name\": \"greet\",\n",
            "      \"confidence\": 0.04438568373413862\n",
            "    },\n",
            "    {\n",
            "      \"name\": \"inform\",\n",
            "      \"confidence\": 0.032039203858483946\n",
            "    },\n",
            "    {\n",
            "      \"name\": \"mood_affirm\",\n",
            "      \"confidence\": 0.028217121964221097\n",
            "    },\n",
            "    {\n",
            "      \"name\": \"mood_deny\",\n",
            "      \"confidence\": 0.027478050676699096\n",
            "    }\n",
            "  ],\n",
            "  \"text\": \"I am very sad. Could you send me a cat picture? \"\n",
            "}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OPlSd-As-Fz4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Evaluating the NLU model on a test data\n",
        "(Here we are using the data at hand i.e nlu.md but it isr recommended to use unseen data)"
      ]
    },
    {
      "metadata": {
        "id": "FmRCylbT4jyw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1483
        },
        "outputId": "fd1bfd57-ebb3-4541-d3b3-b4cbba781164"
      },
      "cell_type": "code",
      "source": [
        "from rasa_nlu.evaluate import run_evaluation\n",
        "\n",
        "run_evaluation(\"nlu.md\", model_directory)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:rasa_nlu.components:Added 'nlp_spacy' to component cache. Key 'nlp_spacy-en'.\n",
            "INFO:rasa_nlu.training_data.loading:Training data format of nlu.md is md\n",
            "INFO:rasa_nlu.training_data.training_data:Training data stats: \n",
            "\t- intent examples: 85 (7 distinct intents)\n",
            "\t- Found intents: 'greet', 'mood_deny', 'mood_great', 'mood_affirm', 'mood_unhappy', 'goodbye', 'inform'\n",
            "\t- entity examples: 18 (1 distinct entities)\n",
            "\t- found entities: 'group'\n",
            "\n",
            "INFO:rasa_nlu.evaluate:Intent evaluation results:\n",
            "INFO:rasa_nlu.evaluate:Intent Evaluation: Only considering those 85 examples that have a defined intent out of 85 examples\n",
            "INFO:rasa_nlu.evaluate:F1-Score:  0.9881730469965763\n",
            "INFO:rasa_nlu.evaluate:Precision: 0.9890756302521008\n",
            "INFO:rasa_nlu.evaluate:Accuracy:  0.9882352941176471\n",
            "INFO:rasa_nlu.evaluate:Classification report: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     goodbye       1.00      0.91      0.95        11\n",
            "       greet       0.93      1.00      0.96        13\n",
            "      inform       1.00      1.00      1.00        10\n",
            " mood_affirm       1.00      1.00      1.00         5\n",
            "   mood_deny       1.00      1.00      1.00         6\n",
            "  mood_great       1.00      1.00      1.00        15\n",
            "mood_unhappy       1.00      1.00      1.00        25\n",
            "\n",
            " avg / total       0.99      0.99      0.99        85\n",
            "\n",
            "INFO:rasa_nlu.evaluate:There were some nlu intent classification errors. Use `--verbose` to show them in the log.\n",
            "INFO:rasa_nlu.evaluate:Model prediction errors saved to errors.json.\n",
            "INFO:rasa_nlu.evaluate:Confusion matrix, without normalization: \n",
            "[[10  1  0  0  0  0  0]\n",
            " [ 0 13  0  0  0  0  0]\n",
            " [ 0  0 10  0  0  0  0]\n",
            " [ 0  0  0  5  0  0  0]\n",
            " [ 0  0  0  0  6  0  0]\n",
            " [ 0  0  0  0  0 15  0]\n",
            " [ 0  0  0  0  0  0 25]]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcb2c069c50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XWEREaqKho6Ojao2Z2RTgbne/Iz5/HDjC3V+uWhIiIlJUrU9IN9S4fRERyaHa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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcb2bf38240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:rasa_nlu.evaluate:Entity evaluation results:\n",
            "INFO:rasa_nlu.evaluate:Evaluation for entity extractor: ner_crf \n",
            "INFO:rasa_nlu.evaluate:F1-Score:  1.0\n",
            "INFO:rasa_nlu.evaluate:Precision: 1.0\n",
            "INFO:rasa_nlu.evaluate:Accuracy:  1.0\n",
            "INFO:rasa_nlu.evaluate:Classification report: \n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "      group       1.00      1.00      1.00        18\n",
            "  no_entity       1.00      1.00      1.00       265\n",
            "\n",
            "avg / total       1.00      1.00      1.00       283\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "Av3R2GZZ-WJO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 2. Teaching the bot to respond using Rasa Core"
      ]
    },
    {
      "metadata": {
        "id": "BKZ63AuS-ZPV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 1. Writing  Stories"
      ]
    },
    {
      "metadata": {
        "id": "W3q1XJ5O4orY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a82511c7-b7e5-462c-c5c2-35df3cabd39a"
      },
      "cell_type": "code",
      "source": [
        "stories_md = \"\"\"\n",
        "## happy path               <!-- name of the story - just for debugging -->\n",
        "* greet              \n",
        "  - utter_greet\n",
        "* mood_great               <!-- user utterance, in format intent[entities] -->\n",
        "  - utter_happy\n",
        "* mood_affirm\n",
        "  - utter_happy\n",
        "* mood_affirm\n",
        "  - utter_goodbye\n",
        "  \n",
        "## sad path 1               <!-- this is already the start of the next story -->\n",
        "* greet\n",
        "  - utter_greet             <!-- action the bot should execute -->\n",
        "* mood_unhappy\n",
        "  - utter_ask_picture\n",
        "* inform{\"animal\":\"dog\"}  \n",
        "  - action_retrieve_image\n",
        "  - utter_did_that_help\n",
        "* mood_affirm\n",
        "  - utter_happy\n",
        "\n",
        "## sad path 2\n",
        "* greet\n",
        "  - utter_greet\n",
        "* mood_unhappy\n",
        "  - utter_ask_picture\n",
        "* inform{\"group\":\"cat\"}\n",
        "  - action_retrieve_image\n",
        "  - utter_did_that_help\n",
        "* mood_deny\n",
        "  - utter_goodbye\n",
        "  \n",
        "## sad path 3\n",
        "* greet\n",
        "  - utter_greet\n",
        "* mood_unhappy{\"group\":\"puppy\"}\n",
        "  - action_retrieve_image\n",
        "  - utter_did_that_help\n",
        "* mood_affirm\n",
        "  - utter_happy\n",
        "  \n",
        "## strange user\n",
        "* mood_affirm\n",
        "  - utter_happy\n",
        "* mood_affirm\n",
        "  - utter_unclear\n",
        "\n",
        "## say goodbye\n",
        "* goodbye\n",
        "  - utter_goodbye\n",
        "\n",
        "## fallback\n",
        "- utter_unclear\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "%store stories_md > stories.md"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing 'stories_md' (str) to file 'stories.md'.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LnD9v_CX-ePm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 2. Defining a Domain"
      ]
    },
    {
      "metadata": {
        "id": "3SzQq1oy5U9T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4fde8c0d-9672-457c-cacc-0e86d3ca5c80"
      },
      "cell_type": "code",
      "source": [
        "domain_yml = \"\"\"\n",
        "intents:\n",
        "- greet\n",
        "- goodbye\n",
        "- mood_affirm\n",
        "- mood_deny\n",
        "- mood_great\n",
        "- mood_unhappy\n",
        "- inform\n",
        "\n",
        "slots:\n",
        "  group:\n",
        "    type: text\n",
        "    \n",
        "entities:\n",
        "- group\n",
        "\n",
        "actions:\n",
        "- utter_greet\n",
        "- utter_did_that_help\n",
        "- utter_happy\n",
        "- utter_goodbye\n",
        "- utter_unclear\n",
        "- utter_ask_picture\n",
        "- __main__.ApiAction\n",
        "\n",
        "templates:\n",
        "  utter_greet:\n",
        "  - text: \"Hey! How are you?\"\n",
        "\n",
        "  utter_did_that_help:\n",
        "  - text: \"Did that help you?\"\n",
        "\n",
        "  utter_unclear:\n",
        "  - text: \"I am not sure what you are aiming for.\"\n",
        "  \n",
        "  utter_happy:\n",
        "  - text: \"Great carry on!\"\n",
        "\n",
        "  utter_goodbye:\n",
        "  - text: \"Bye\"\n",
        "  \n",
        "  utter_ask_picture:\n",
        "  - text: \"To cheer you up, I can show you a cute picture of a dog, cat or a bird. Which one do you choose?\"\n",
        "\"\"\"\n",
        "\n",
        "%store domain_yml > domain.yml"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing 'domain_yml' (str) to file 'domain.yml'.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ROL3AYs5-iCg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Custom Actions"
      ]
    },
    {
      "metadata": {
        "id": "7SbmLMJa5X0E",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from rasa_core.actions import Action\n",
        "from rasa_core.events import SlotSet\n",
        "from IPython.core.display import Image, display\n",
        "\n",
        "import requests\n",
        "\n",
        "class ApiAction(Action):\n",
        "    def name(self):\n",
        "        return \"action_retrieve_image\"\n",
        "\n",
        "    def run(self, dispatcher, tracker, domain):\n",
        "        \n",
        "        group = tracker.get_slot('group')\n",
        "        \n",
        "        r = requests.get('http://shibe.online/api/{}?count=1&urls=true&httpsUrls=true'.format(group))\n",
        "        response = r.content.decode()\n",
        "        response = response.replace('[\"',\"\")\n",
        "        response = response.replace('\"]',\"\")\n",
        "   \n",
        "        \n",
        "        #display(Image(response[0], height=550, width=520))\n",
        "        dispatcher.utter_message(\"Here is something to cheer you up: {}\".format(response))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "M-jn1g_k-o-m",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##  Visualising the Training Data"
      ]
    },
    {
      "metadata": {
        "id": "pXZhTaSw9SNR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "!apt-get -qq install -y graphviz libgraphviz-dev pkg-config;\n",
        "!breq install graphviz\n",
        "\n",
        "!{python} -m pip install pygraphviz;"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "O1gYRXe15amU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1090
        },
        "outputId": "9c0838e3-56c1-4eeb-a879-cc09619269d3"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from IPython.display import Image\n",
        "from rasa_core.agent import Agent\n",
        "\n",
        "agent = Agent('domain.yml')\n",
        "agent.visualize(\"stories.md\", \"story_graph.png\", max_history=2)\n",
        "Image(filename=\"story_graph.png\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:apscheduler.scheduler:Scheduler started\n",
            "Processed Story Blocks: 100%|██████████| 7/7 [00:00<00:00, 248.31it/s, # trackers=1]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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HHDhwgD///JPTp09z8q+TnPzrJKf/Os2FlAukJKcAcD7+PADe+bxx93TH3d2dfAXyUaBA\nAUoULUGJIiUICAhg0aJFtG3blueff56KFSvi5+fntK9J6/lERERExDCVvZwuIyODmTNnMmTIEGw2\nW46bPrh3714CAwOZOXMmXbt2zdZ7d+vWjWPHjhndLVPr+URERETEEJW9nGzVqlUMHDiQmJgYXnzx\nRV5//fUcVyQef/xxIiMjiYqKws0te08CWbJkCZ06deLAgQOUK1cuW+99K7SeT0REREQM2K1z9nKg\n48eP89hjj9GqVSvKly9PVFQU//vf/3Jc0du7dy/z5s3jjTfeyPaiBxAaGkqRIkWYM2dOtt/7Vri7\nu9OnTx/27NlDr169ePnll6lfv77O5xMRERERh1LZy0EyMjKYPn06NWrUYN26dcyfP58ffvjB4Ucp\nOMqIESOoUqUKjzzyiEPu7+7uTlhYGDNmzHDI/W9VoUKFGDNmDDt27KBUqVLcf//9dOzYkf3795uO\nJiIiIiK5kMpeDrFp0ybq169P7969ee6554iJiSEsLMx0rNvm6FG9yx5//HF2797Ntm3bHPaMW1W1\nalUWL17MypUriY2NJTAwkAEDBnDu3DnT0UREREQkF1HZc3HJycm89NJLNGrUCH9/f7Zv387o0aPx\n9fU1He2OOHpU77LGjRtTuXJlZs2a5dDn3I5WrVoRGRnJpEmTmDNnDhUrVmTChAlZx1CIiIiIiNwJ\nlT0XtmHDBurWrcu0adP4+OOPWbVqFffcc4/pWHfMWaN6l3Xt2pXZs2e7ZIm62nq+WrVqsWLFCtPR\nRERERCSHU9lzQQkJCQwYMID77ruPSpUqERUVRZ8+fbBYLKajZQtnjepd9uSTTxo/guFGLq/n27lz\nJxUqVCA0NFTr+URERETkjqjsuZjw8HCqV6/O3LlzmT17NkuWLKF06dKmY2UbZ4/qAVSuXJl69eq5\n5FTOf6pSpYrW84mIiIhItlDZcxEpKSkMHDiQNm3a0LBhQ3bt2sWjjz5qOla2c/ao3mWPP/44CxYs\nICUlxanPvV1azyciIiIid0plzwXs3LmTRo0a8eWXXzJlyhTmz59PQECA6VjZzsSo3mWPPfYYycnJ\nLFmyxKnPvRNazyciIiIid0Jlz6CMjAzeffdd6tWrR8GCBdmxYwd9+vQxHcthTI3qARQtWpQWLVrk\niKmc/6T1fCIiIiJyO1T2DDl58iRt27ZlxIgRjBo1itWrV1O2bFnTsRzG5KjeZY8//jjLli0jLi7O\nyPPvlNbziYiIiMitUNkzYM2aNdStW5e9e/eyZs0ahgwZYqwAOYvJUb3LunTpgru7OwsWLDCWITto\nPZ+IiIiI3Izc3TBcjN1uZ+zYsbRq1YoGDRoQGRlJo0aNTMdyOFcY1QMoUKAAnTp1Yu7cucYyZBet\n5xMRERGRG1HZc5LTp0/TqlUrRowYwUcffcTChQspVKiQ6VhO4QqjepeFhYWxbt06jh8/bjpKttB6\nPhERERG5FpU9J9i6dSv16tUjNjaWX375hb59+5qO5DSuMqp3Wbt27fD19eX77783HSVbaT2fiIiI\niPyT+e++c7m5c+fStGlTypcvz6+//kqdOnVMR3IqVxrVA/D19aV9+/bMnz/fdBSH0Ho+EREREblM\nZc9BbDYbr776Kt26deOJJ55g5cqVFC1a1HQsp3K1Ub3LwsLCWLt2LadOnTIdxSGutp6vZs2aLF++\n3HQ0EREREXEi1/kOPBdJSEigffv2fPTRR8ycOZNPP/0UDw8P07GcztVG9S77z3/+g7e3NwsXLjQd\nxaH+vp6vRo0atGvXTuv5RERERPIQlb1sduTIEZo2bUpUVBTr16/n8ccfNx3JCFcd1YPMqZzt2rXL\n8Ucw3KwqVarwzTffsHLlSg4dOqT1fCIiIiJ5hGt9F57D7dy5k8aNG2Oz2fjll18IDg42HckYVx3V\nuywsLIyIiIhcO5Xzalq1asXWrVu1nk9EREQkj1DZyybh4eE0bdqUypUrs2HDBsqWLWs6kjGuPKp3\nWceOHfH29s51u3LeiNbziYiIiOQdrvmdeA7z9ddf065dOzp37syyZcsoWLCg6UhGufqoHmRO5Wzb\ntm2emcr5T1rPJyIiIpL7qezdocmTJ9OzZ09efvllvvrqKzw9PU1HMionjOpdFhYWxurVqzl9+rTp\nKMZoPZ+IiIhI7uXa3427uPfee4/+/fszYsQIRo8ejcViMR3JuJwwqndZx44d8fDw4IcffjAdxTit\n5xMRERHJfSx2u91uOkRONHbsWIYOHcoHH3zAwIEDTcdxCXv37iUwMJCZM2fStWtX03FuSpcuXbhw\n4QLLli0zHcVlxMfHM3bsWMaPH0/FihX54IMPCA0NNR1LRERERG7Nbo3s3SK73c7gwYMZNmwYn3/+\nuYre3+SkUb3LwsLCWLVqFWfOnDEdxWVoPZ+IiIhI7qCyd4teeeUVJk6cyMyZM3n66adNx3EZOWmt\n3t916tQJq9Wa53blvBlazyciIiKSs2ka5y0YMWIEb7/9NlOnTuWZZ54xHcelPP7440RGRhIVFZWj\nyh5A586dSU9PZ+nSpaajuKz09HS++OILXn/9dTIyMhg+fDj9+/fHarWajiYiIiIiV6dpnDdrwoQJ\nvP3223z88ccqev+QU0f1LgsLC2PlypWaynkdOp9PREREJOfJed+ZG/DFF18waNAgxo4dy3PPPWc6\njsvJiWv1/q5Tp064ubmxePFi01FcntbziYiIiOQcKns3MGfOHHr37s3IkSMZMmSI6ThGzZw5k8jI\nyCtey+mjegD+/v60bNmSRYsWmY6SY2g9n4iIiIjry5nfnTvJzz//zNNPP82LL77IsGHDTMcxbvr0\n6QQHB9OxY8es0pfTR/Uu69SpEz/99BPJycmmo+QoOp9PRERExHVpg5ZrOHjwIA0bNqR+/fp8//33\n2ogCKF++PLGxsbi7u2Oz2WjZsiWrV69m1qxZOeZcvWs5efIkJUuWZNGiRXTs2NF0nBxJ5/OJiIiI\nuBRt0HI1Z86coV27dpQuXZp58+ap6JF5vuCxY8eAzJ0Z7XY7a9asISMjg88++4zNmzcbTnhnihUr\nllXs5fZoPZ+IiIiIa1HZ+4fU1FQefPBBUlJSWLJkCfny5TMdySUcP36c1NTUK15LT08HYP369dSv\nX58WLVrk6NLXuXNnFi9erCmId0jr+URERERcg8rePwwcOJDIyEiWLl1KiRIlTMdxGbGxsde8drn0\nrVmzhmbNmrF7924npcpenTt35tSpU/z++++mo+QKWs8nIiIiYpbK3t/MmTOHKVOmMG3aNGrUqGE6\njks5ePDgdXfbdHNzw8vLi8WLFxMYGOjEZNmnWrVqVKlSRVM5s9Gdns8XFxfn4IQiIiIiuZfK3iVR\nUVH07t2bwYMHExYWZjqOyzl48CAeHh5XvWa1WvH09GTp0qW0bNnSycmyV8eOHVm4cKHpGLnO7azn\ni46OplatWlrzJyIiInKbVPaA8+fP88gjj1CzZk3eeecd03Fc0sGDB686/c5qteLt7c3q1atp0aKF\ngWTZq3Pnzuzdu5c9e/aYjpIr3cp6vkGDBnHs2DFat27NmTNnDKQVERERydlU9oBnnnmGuLg4FixY\ngKenp+k4Lmnfvn1Za/Mus1qt+Pr6snr1aho1amQoWfZq3LgxRYoU4YcffjAdJVf7+3q+uXPn/ms9\nX3h4eNZUzyNHjtChQwcuXrxoMrKIiIhIjpPny97XX3/NggULmD17NqVKlTIdx2X9cyqdu7s7BQoU\nYP369TRo0MBQquxntVoJDQ1l6dKlpqPkepfX80VHR9OrVy9eeeUVatasydKlS+nfv3/WkSdpaWls\n2rSJJ554Ah0LKiIiInLz8vSh6seOHaNGjRr06NGD8ePHm47jsmw2G15eXlmjLu7u7vj5+bFmzRpq\n1qxpOF32mzdvHk888QSnT5+mYMGCpuPkGdHR0bz00kusWrWKtLQ0MjIyrrju5ubG0KFDGTVqlKGE\nIiIiIjnK7jxd9tq3b8+ePXvYsWOHztO7jtjYWMqXLw+Ah4cH/v7+rF27lmrVqhlO5hjx8fEULVqU\n2bNn8/DDD5uOk6fEx8dTrlw5EhISrvmejz/+mOeff96JqURERERypN15dhrn1KlTWbFiBV999ZWK\n3g1cPmPParVStGhRfv/991xb9CBz58hGjRppKqcBI0aMICUl5brveeGFF1ixYoWTEomIiIjkXHly\nZO/o0aPUqFGDPn36MHbs2Bu+f/fu3ezcudMJyVzTmjVr+OSTTwgICOCtt94iICDAdKR/8fPzIzQ0\nNNvuN3bsWMaNG8eJEyeue76gZJ/o6Ghq1Khxw0PXL5/p+Ouvv1K7dm3++OMPoqKinJQyZ8ruvx8i\nIiKSI+TNaZwdOnTgwIEDbN26FW9v7xu+f+TIkbzxxhtOSCa3q3LlKsTEZN9xCVFRUdSsWZPff/89\nV21A48ratWvHihUrbmoTFnd3dwoXLsyWLVuYNm0ab775phMS5lyVq1QlZk+06RgiIiLiXLvdTSdw\ntu+++44ff/yRVatW3VTRu6x0+YpMWLbegclc13efTeT+Bx7hrqLFTEe5qvkfj2fzskXZes8aNWpQ\nrlw5li5dqrLnBImJidSoUYOLFy+ybds24uPjAfDy8iI9Pf1fo33p6enExcXRtm1bOnfuTJkKlfjw\nx3Umoru8+ZM/YMsKHSUiIiKSF+WpspecnMxLL71Ejx49aN68uek4OUaX3v2xWCymYzhdu3btWLp0\nKW+99ZbpKLlegQIF+N///pf1v48fP8727dvZtm0b27ZtY8uWLRw4cICMjAysVivu7u5cvHiRXbt2\nceHCBchz8xNEREREbixPlb3hw4eTkJBwU+v05P/lxaIHmbu1TpkyhWPHjlGyZEnTcfKUEiVKUKJE\niSvWmaWkpBAVFcW2bdvYvn07mzdvZufOnezfv5/8fv4G04qIiIi4pjxT9nbu3MnEiROZPHkyRYsW\nNR1HcoAWLVrg7e3N8uXLefrpp03HyfN8fHyoX78+9evXz3rNbrczePBgZs6Zy5lTJ112qrGIiIiI\nCXlim8GMjAyee+456tSpwzPPPGM6juQQvr6+NGvWjJ9++sl0FLkGi8VCoUKF8MmXX0VPRERE5B/y\nRNn7/PPP2bhxI59++qm20Zdb0qZNG3766acbHgcgIiIiIuJqcn3ziYuLY9iwYQwYMICgoCDTcSSH\nadOmDfHx8WzZssV0FBERERGRW5Lry96QIUPw8fFhxIgRpqNIDlSjRg1Kly6tqZwiIiIikuPk6rK3\nZcsWvv76az744APy589vOo7kUK1atVLZExEREZEcJ9eWPbvdTr9+/WjcuDEPPfSQ6TiSg7Vp04Zf\nf/2Vc+fOmY4iIiIiInLTcm3Z+/rrr9m8eTOTJk3Ks+fESfZo3bo1GRkZrFmzxnQUEREREZGblivL\nXmJiIq+99hrPPvsstWvXNh1HcriAgADq1KmjqZwiIiIikqPkyrI3cuRILly4wFtvvWU6iuQSbdu2\nVdkTERERkRwl15W9/fv389FHHzFq1CgCAgJMx5FconXr1uzbt48DBw6YjiIiIiIiclNyXdkbMGAA\nlSpVok+fPqajSC7SuHFj8ufPz8qVK01HERERERG5Kbmq7K1YsYKlS5cyceJE3N3dTceRXMTT05OQ\nkBAiIiJMRxERERERuSm5puzZbDaGDBnCAw88QPPmzU3HMWrh1Mk8EVyFZ5pk3+Y0G5Yu4vmW99K1\nVjki10dc9bUZ749i+JMPZtszXU3z5s2JiIjAbrebjiIiIiIickO5Zvjrq6++Yvfu3cybN890FOO6\n9O5HgYKFmPPh2Gy53/mEc0x6bRBvfTWfpMQELiQnXfW13K5Fixa88sor7Nq1ixo1apiOI7nIjl/W\nc/TgPto93tN0FBEREclFckXZS0lJ4a233uLZZ58lMDDQdByXkJ1nCx6PPUDaxYuUrliFfH5+AOzd\nEfmv1xq17ZBtz3RFderUoVChQkRERKjs5UE2WzprFy2gWv2GFL+7XLbee+X8WVSrd2+23lNEREQk\nV5S9cePGER8fz+uvv246CgBzJ77Pt1Mm4FeoMG5uFhLiz+BmtVK6QmWSz8a9tfoAACAASURBVCdy\nLu4vbLZ07uv0EM+9/T8ATh87wudvDyNq4y94entTrV5Deg0fRaEixW7q+tm400wZPoQdv6wnw56B\nb/4CWLj5wvfDl1NY/OWnnDtzhkJFitLjlTdoHNqRX5YvZurbrwHQvcE9AAwa9zHTRg+/4rXBH0xh\nypsv4+3ry9S1W5k9fgzfTZ1El1798M6Xj/BvZlHunmpsXhN+S78vrsRqtdK0aVMiIiJ44YUXTMcR\nBzu8bw/TRg9n387t2DMy8Pbx5d427WnxUNer/vl+ZfKX+OTLx6cjXiVm+1YK+Bck7PmBtAzrBsDJ\nw4euem3ysMH8unwxvyz7gc9HDmPahm0UDChq+KsXERGR3CDHr9k7ffo0//vf/3j11VcpXry46TgA\ndH3hv1Rv0IhajUIYv2QN0zfuJijkfpLPJzL889l89dsu+r/zISu/mcXxQwcBGDfoOSxuFib/9DP/\n+3Y5SYnnGNv/max73uj6O892J/FsPBN+XMusrXvp0rv/LWWOO3Gc16fOZtaWGBqHdmT6eyMBaBza\nkZcnTgNg+sZovo0+Rsh/HvjXa03ad+KJl17Lut9jg16lWvC97N0ZSWDdBrR7oieP9L/13xdXc3nd\nns1mMx1FHGz8S33xvyuAqWs2M2HpWgoUuou01IvA1f9822zpvP1MN0qULc/UNZt57u33+GT4f9m7\nI5IMm+2a1/qN/oCAEqXoNXw030YfU9ETERGRbJPjy96bb75J/vz5GThwoOko/1Kg0F3k9/PHy8eX\nus1acCE5meJ3l8Pdw4O6zTI3kTlz6gQH/4hi7/atdBv4CgUDihJQohSPDxrK3u1bOfhH1A2v74/a\nzv6o7XQfMpwiJUtjtbrjky//LWXtOfQtylYNxMPLiypBdTkbdzpbfg/K31ODavUb0qnnc5QPrH5L\nvy+uqEWLFpw9e5bt27ebjiIOZLOl82dMNA1ahuKTvwABJUoSGNyAE3/GXvG+v//5Tk9L48SfsTz0\n7Iv45C9A7Sb3Ufzucuz4dT17d2675jURERERR8nR0zhjYmL4/PPPmTJlCvny5TMd57osFjf42y6O\nFrdLPdtu58ThWACKlymbdb10hUoAWdeudz09LQ2AcvdUu+1830/7mJXfzOLMyROkXryAu4fHbd/r\nVlzv98UV1axZkyJFihAREUHdunVNxxEHsVrdKVKqDBtXLadus+YkJSaya9OvBIXcf83POX30MAC9\n77vyz0XSuXPXvSYiIiLiKDm67A0dOpSqVavSo0cP01Gyxd+39M/IyPy1xWLJev1a19PTUgGwut9e\nQdu18RdmvD+alz78lKAm9xG5PoKPXnnxtu6V21ksFu677z4iIiJ46aWXTMcRBxrw3kRG9XmCHg1r\nkK9AAeo0a0G3F4dc8/2Xf0Ayc/MefPIXuOLabz8tveY1EREREUfJsdM4t27dysKFCxk9ejRWq9V0\nnDtS/O7yAJw8/GfWa0cP7s26dqPrAcVLAXD80IHbev6R/XspVLQYjdp2wCd/gf8fXZOrat68OevW\nrSPt0oiq5D52u53ls7/i8UGv8k3UIb78NYoXx3503aIWUCLz7+GR/Xtv6ZqIiIiIo+TY7+rfeOMN\n6tatS8eOHU1HuWPlA6tTpXYwcyaM5exfp/jr+DFmjx9LldrBlLun2g2vV6t/L4WLl+DrsW9nXd+y\nZuVNP79Q0eIknInjyP69nD93lv1RWo92Pc2bNycxMZGtW7eajiIOYrFY8PDy4vORw3jonpI8dE9J\nHq1ZlmGPdeb0sSNX/ZyKNWpTqkIlvhwzglNHD5Nhs3Hm1ElSks5f9xqAp7c3+3ZuIy01lbSLF535\npYqIiEguliPL3ubNm/nxxx8ZNWpUtp4nl13mTRrHro2/8tO8GcybNI4NSxfx9XtvczbuNEMebJv1\nX4APh/TnxJ+xDPrgY2w2G31bNeLF9s3w9PZm0AcfZ93zetet7h4M/WQ6SQkJ9G3dmFG9H8e3gD9n\n404z/qW+N8xbp2lzgprcz5CH2vJmjzC8fHxJS03l7We6sW/nNsa/9DwAgzu3ZNmsL6/62rdTJjD9\nvZGcOXmCwZ1a8snw/7J760aWzfqCycMG3/bviyu65557KFq0KBs2bDAdRRzJbmfconC+jT7Gt9HH\nmLk5Bt8Cfiz8bBKzPxz7rz/fFouF16ZMx93DkxfbN6NHw+p8+e6bXLyQct1rAK0feYJfli+mf9sm\n7N+1w+RXLSIiIrmIxW530Z0wrqNNmzYkJSXx888/O+V5I0eO5LMvv2bCMu2c54rmfzyezcsWsTcm\nxmnP7NKlCxaLhe+++85pz5Sre/vtt/n86xl8+OO6bLvn3u1bmTRsMBOWrLni9UlDB2GxQL93xmfb\nsxxt/uQP2LLiB2L27DEdRURERJxrd47boOXnn39m5cqVrFq1ynSUHOHU0cM83/Le677nk1W/U7RU\nGSclyh2aNGnC2LFjsdvtLjm6LHfG09uHU0f+ZOdvG7inbgNSzify609LWb/kO179+CvT8URERERu\nSo4re6+//jpNmzalRYsWpqPkCEVLleHb6GOmY+Q6ISEhDBkyhJiYGKpWrWo6jmSzslUDeerVEXz2\n1lBOHzuC1epO+Wo1GDLxc+o0bW46noiIiMhNyVFlb9WqVaxZs4a1a9eajiJ5XHBwML6+vqxfv15l\nL5dq27U7bbt2Nx1DRERE5LblqA1aRowYQZs2bWjWrJnpKJLHeXh40KBBA6etGxURERERuVU5ZmRv\n3bp1bNiwgfXrtUmKuIaQkBDmzp1rOoaIiIiIyFXlmJG9d999l2bNmhESEmI6igiQuUnLvn37OH78\nuOkoIiIiIiL/kiPK3vbt21mxYgVDhw41HUUkS+PGjbFarZrKKSIiIiIuKUeUvdGjR1OrVi3atm1r\nOopIFj8/P2rVqqWyJyIiIiIuyeXX7O3bt4/vvvuOOXPm6DwzcTkhISFs2LDBdAwRERERkX9x+ZG9\nd955h/Lly/Pggw+ajiLyLw0bNmT79u2kpKSYjiIiIiIicgWXLnuHDx9m1qxZvPbaa1itVtNxRP7l\n3nvvJS0tjcjISNNRRERERESu4NJlb+LEiRQtWpQnnnjCdBSRq6pQoQIBAQFs3LjRdBQRERERkSu4\nbNlLTk5m2rRp9OvXDw8PD9NxRK7KYrFQv359lT0RERERcTkuW/a++OILUlJS6N27t+koItfVoEED\nlT0RERERcTkuWfbsdjuTJ0+me/fuFC5c2HQcketq0KABBw4c4K+//jIdRUREREQki0uWvR9//JHo\n6Gj69etnOorIDd17770AbNq0yXASEREREZH/55Jlb8KECbRp04aaNWuajiJyQ4ULF6ZChQr8/vvv\npqOIiIiIiGRxuUPVd+3aRXh4OEuWLDEdReSmNWjQQCN7IiIiIuJSXK7sTZo0icqVKxMaGmo6yhWS\nk87zy7IfTMeQq/hz7x7TEWjQoAGjRo3CbrdjsVhMx8lz9Pfz2v7cF2M6goiIiBjiUmUvKSmJ2bNn\n88Ybb+Dm5lozTM+cOsm4Qc+ZjiHXULlKFaPPb9CgAXFxcRw4cICKFSsazZIXxZ08ob+f11GlalXT\nEURERMQAi91ut5sOcdkXX3zB888/z5EjRyhSpIjpOLnSyy+/TEREhKYcZrOUlBT8/PyYOXMmjz76\nqOk4kostX76cBx54gN69e/PRRx9pJFlERESuZbdLDZ9NnTqVBx98UEXPgZKSksiXL5/pGLmOj48P\ngYGBbN++3XQUyeVCQ0OZM2cOU6ZMYfDgwabjiIiIiAtzmbK3c+dOfvvtNx2i7mDJycn4+vqajpEr\nBQUFERkZaTqG5AFdunRhzpw5TJo0if/+97+m44iIiIiLcpk1e1OnTqVChQrcf//9pqPkahrZc5za\ntWvz3nvvmY4heURYWBjJycn07NkTPz8/3njjDdORRERExMW4RNm7cOECs2bNYsiQIS63MUtuk5SU\npGmyDhIUFMSpU6c4ceIExYsXNx1H8oDu3btjs9no1asXHh4eDB061HQkERERcSEuUfa+/fZbEhMT\neeqpp0xHyfU0jdNxgoKCANi2bZvLHR0iuVfPnj2x2Wz06dMHq9XKyy+/bDqSiIiIuAiXKHszZ84k\nNDRUoyFOoGmcjlO4cGHKlCmjsidO16tXL5KSkhg0aBD58+enb9++piOJiIiICzBe9k6fPk14eDgz\nZswwHSVPSEpK0sieAwUFBWlHTjFiwIAB2Gw2+vfvj9Vq5dlnnzUdSURERAwzXvbmzp2Ll5cXHTt2\nNB0lT0hOTtbIngMFBQUxf/580zEkjxo8eDAJCQn07dsXX19fnnzySdORRERExCDjZW/OnDk8+OCD\nKiBOopE9xwoKCmL06NGcP3+e/Pnzm44jedCIESNIS0ujZ8+eWK1WHnvsMdORRERExBCjZe/QoUP8\n9ttvDB8+3GSMPEVr9hwrKCiIjIwMoqKiaNiwoek4kkeNHj2a9PR0unfvjtVq5dFHHzUdSURERAww\nWvZmzJhBQEAArVq1Mhkjz7Db7aSkpKjsOVD58uXx9/dn27ZtKnti1JgxY0hKSuLJJ5/E19dXU+VF\nRETyIKNlb+7cuTzyyCN4eHiYjJFnpKSkYLfbNY3TgSwWCzVq1GDnzp2mo0geZ7FYmDhxIjabjbCw\nMBYuXEj79u1NxxIREREnMnaCeXR0NLt27dL0IidKSkoC0MiegwUGBhIdHW06hggWi4WPP/6Y7t27\nExYWRkREhOlIIiIi4kTGyt7ChQspWrQojRs3NhUhz1HZc47AwED++OMP0zFEgMzCN2XKFLp06UKH\nDh1Yu3at6UgiIiLiJMbK3vfff0/Hjh2xWq2mIuQ5ycnJAJrG6WDVqlXjxIkTnDlzxnQUEQCsVivT\np0+nY8eOdOrUid9//910JBEREXECI2Xv5MmTbNq0ic6dO5t4fJ6lkT3nCAwMBNBUTnEpVquVGTNm\ncP/999O2bVs2b95sOpKIiIg4mJGyt2jRIry9vWnZsqWJx+dZKnvOcffdd5M/f352795tOorIFTw8\nPJg/fz4hISG0bt2arVu3mo4kIiIiDmSk7H3//fe0bdtW0wmdTNM4ncNisVC1alWVPXFJnp6ezJ8/\nn7p16xIaGsquXbtMRxIREREHcXrZS0xMZPXq1ZrCaUBSUhIWiwUfHx/TUXI9bdIirszHx4fFixdT\nrVo1WrRooR9MiIiI5FJOL3sRERGkpaXpvCcDkpKS8Pb21qY4ThAYGKhvoMWl+fr6snTpUqpWrUqb\nNm3Yv3+/6UgiIiKSzZxe9sLDwwkKCqJIkSLOfnSel5ycrPV6TlKtWjUOHTrE+fPnTUcRuaZ8+fKx\nePFiSpQoQfPmzYmNjTUdSURERLKR08veypUradWqlbMfK2SO7Gm9nnMEBgZit9uJiYkxHUXkuvz9\n/fnpp58oUqQIrVu35ujRo6YjiYiISDZxatk7evQo0dHR2oXTkKSkJI3sOUnFihXx8vLSuj3JEQoW\nLMjy5cvx8vKiefPmHD9+3HQkERERyQZOLXsrV67Ey8uLkJAQZz5WLtE0Tudxd3enfPny7Nu3z3QU\nkZtSpEgRVq9ejbu7O82bN+fkyZOmI4mIiMgdcmrZW7VqFU2aNNFUQkM0jdO5ypcvz8GDB03HELlp\nRYsWZeXKlaSnp9OmTRvi4uJMRxIREZE74LSyZ7fbWbVqldbrGaSRPeeqUKECBw4cMB1D5JaUKlWK\niIgIEhMTadWqFfHx8aYjiYiIyG1yWtnbu3cvx48f5/7773fWI+UftGbPucqXL6+yJzlSmTJliIiI\nID4+nvbt25OYmGg6koiIiNwGp5W9X3/9FS8vL+rWreusR8o/JCcnaxqnE1WoUIHjx4+TkpJiOorI\nLStbtiwrV67k0KFDhIaG6hgRERGRHMipZS84OBgvLy9nPVL+QSN7zlW+fHnsdrvOLpMcq3LlykRE\nRHDgwAEeeOAB/eBCREQkh3Fq2WvUqJGzHidXobLnXBUrVgTQJi2So1WtWpUVK1awbds2HnjgAS5c\nuGA6koiIiNwkp5S9xMREdu3apbJnmKZxOleBAgUoXLiw1u1JjlerVi3Cw8PZvHkzXbp04eLFi6Yj\niYiIyE1wd8ZDNm7ciM1mo2HDhs54nAA//PADp06dwt/fHx8fH7y9vTlz5gyJiYkcOHCAAgUK4OXl\nhZ+fn+mouVqFChU0sie5QlBQEEuXLqVNmzY89thjzJs3D3d3p/xfiIiIiNwmp/w/9e+//87dd99N\nqVKlnPE4IXPa7JgxY/71+vjx4xk/fvwVr23evJng4GBnRctTtCOn5CYNGzZk+fLltG3blm7dujFn\nzhwVPhERERfmlGmcW7dupV69es54lFzSsWPHG77HYrFQs2ZNFT0H0sie5DaNGzdm2bJlLFu2jF69\nepGRkXHV96Wmpjo5mYiIiPyTU8rejh07qFWrljMeJZc0bNiQwoULX/c9bm5u9OvXz0mJ8iYdrC65\nUUhICAsXLmTevHn07t37X4XvzJkzhISEEB0dbSihiIiIgBPKXkpKCgcOHKBmzZqOfpT8jZubGx07\ndsTDw+Oa7/Hw8KBbt25OTJX3lClThsTERM6dO2c6iki2at26NQsXLmTWrFkMGDAAu90OwOnTp2nW\nrBmbNm1i7NixhlOKiIjkbQ4ve1FRUdhsNpU9Azp16kR6evpVr3l4ePD4449rgxYHK1GiBADHjx83\nnEQk+4WGhjJnzhymTJnC4MGDOXHiBCEhIcTExAAwc+ZMjh49ajiliIhI3uXwsrdz5058fHyoUKGC\nox8l/9C2bVs8PT2vei0tLY0+ffo4OVHeo7InuV2XLl2YPn06kydPpmHDhhw8eJC0tDQgc13whx9+\naDihiIhI3uWUsle9enWsVqujHyX/4OvrS/Pmzf/1e2+xWAgMDKRBgwaGkuUdRYoUwcPDg2PHjpmO\nIuIwTZo0oVixYhw7diyr6EHmD5UmT57M2bNnDaYTERHJu5wyjVNTOM154IEH/vWaNmZxHovFQvHi\nxTWyJ7nWoUOHCAkJ4cSJE1cUvcvS0tL45JNPDCQTERERh5e9mJgYqlat6ujHyDV07NjxXzvlWa1W\nHnvsMUOJ8p4SJUqo7EmutHfvXu69915OnDhxzfXB6enpjBs3jgsXLjg5nYiIiDi07KWmpnL06FGt\n1zOoZMmSVxx7cXkHzkKFChlMlbeo7EludOHCBbp06cLJkyevOqL3d/Hx8UyfPt1JyUREROQyh5a9\n2NhYbDabyp5hDz74YNYRDNqYxflKliypNXuS63h7e/Pbb78xZswY/Pz8cHd3v+Z77XY7o0aNwmaz\nOTGhiIiIOLTsXT5MWmXPrE6dOmX95L1q1ao0btzYcKK8RSN7klvlz5+fV155haNHj/L+++9z1113\nXbX02e12jhw5wsKFCw2kFBERybscXvYKFiyoKYOGBQUFUbJkSQD69u1rOE3eo7InuV3+/PkZMGAA\nhw4dumbps1gsjBo1ylBCERGRvOna826ywcGDB6lYsaIjHyFkroc5e/Ys586d4+LFiyQmJmZdO3/+\nPGlpadSqVYvTp09TunRpwsPDAfDy8sLX1xc/Pz/8/f0pWLDgNc/lk9tXokQJEhMTOX/+PPnz5zcd\nR8RhLpe+Z555hmnTpvH222+TkJBAeno6GRkZbN++ndWrV9OiRYtbvvflf+fi4+NJSUkhJSUFyNwA\n5u//5hUoUCCraPr4+ODt7U2hQoWyPkRERPISi91utzvq5mFhYQAsWLDAUY/ItdLS0oiNjWX//v0c\nP36cw4cPc+LECY4cOcLx48c5ffp0VsHLTj4+PhQsWJC77rqLkiVLUqJECUqXLk3x4sUpU6YMZcqU\noUqVKhQoUCBbn5ubRUZGUrduXWJiYqhcubLpOCJOk5iYyKRJkxg7dixJSUmkp6fTokULVq1alfWe\nCxcuEB0dTWxsLH/++SdHjhzhyJEjWf/mxcXFER8fn22ZChUqROHChbP+TStdujSlS5fm7rvvply5\nctxzzz14e3tn2/NEREQM2u3QstekSRPq16/Phx9+6KhH5HgpKSns3LmTbdu2ER0dTUxMDDExMcTG\nxmats8uXLx9lypShePHiWcWrWLFiWaNxBQsWzPq1h4cHBQsWzLr/5Z9sp6amsmPHjitGWpOTk7l4\n8SLnzp3LKo6Xfx0fH8+RI0c4ceIEhw8f5uTJk5w8eTLrc4sXL07VqlWpXLkylStXJigoiKCgIIoW\nLeq837wc4s8//6Rs2bL89ttv3HvvvabjiDhdYmIiEydOZMyYMSQmJtK9e3fi4uLYvXs3sbGxWcfD\nFC9enFKlSlGqVCnKli1L8eLFKVy4MIUKFcpaElCoUCG8vLyyRsnd3Nzw9/fPeta5c+ey7nf+/Hku\nXrxIfHz8FSODcXFxnDhxgkOHDnH06FGOHj3KiRMnsu5Xrlw5AgMDCQwMpGbNmtSvX5+qVavi5ubw\n04pERESyk2PLXuXKlXnqqacYNmyYox6Ro6SnpxMZGcmGDRvYunVrVsFLT0+nQIECBAYGUrly5awS\nVaVKFSpWrHjFNzImpaamcujQIfbu3cuePXvYu3cvMTEx7NmzhyNHjgBQqlQp6tSpQ1BQEI0aNSIk\nJAQ/Pz/Dyc1KSEjA39+fZcuWERoaajqOiFMkJCSwdu1a1q9fz+bNm9myZQsJCQm4u7tTqFAhWrdu\nTbVq1bjnnnsIDAykYsWKeHl5Gct78eJF9u/fz+7du4mOjuaPP/4gOjqaXbt2cfHiRfz8/AgODqZ+\n/fqEhIRw33335fl/20RExOU5tuz5+fnxwQcf0KtXL0c9wqXZbDY2btzImjVrWLduHT///DOJiYkE\nBAQQHBxMnTp1sj4qVaqExWIxHfm2/fXXX0RGRl7xsWfPHqxWK7Vr16ZZs2bcd999NG/e3GXKq7PY\n7XY8PDyYOXMmXbt2NR1HxCFsNhs///wz4eHhrFq1io0bN5KRkUH16tWpX79+1ketWrVIT0/Hx8fH\ndOSbkpaWxo4dO9i0aVPWx65du3Bzc6NBgwa0bNmSVq1a0aRJE6xWq+m4IiIif+e4snfhwgV8fHz4\n/vvv6dSpkyMe4ZKSkpJYvXo1S5Ys4YcffuDEiRMUK1aMZs2a0aRJE0JCQqhTp06emA506tQpfv/9\n96xvACMjI7FYLDRs2JCOHTvSqVMnAgMDTcd0isKFCzN69Giee+4501FEso3NZuPXX39l/vz5fPPN\nN5w4cYISJUoQEhJCq1at6NChQ9ZOwLnJX3/9RUREBOHh4YSHh3PgwAHuuusu/vOf//Dwww/Ttm1b\nbXYlIiKuwHFl79ChQ5QrVy5PrFNKTk5m0aJFzJgxg1WrVpGRkUGTJk3o0KEDHTp0yDOF5kbi4uJY\nsWIFixcvZsWKFcTHx1OtWjUee+wxnnjiCcqWLWs6osNUrFiR3r178+qrr5qOInLHtm7dytSpU5k7\ndy5nz56lXr16PPTQQzz44INUqVLFdDyni4mJ4dtvv+Xbb79ly5YtFCxYkK5du9K7d2/q1q1rOp6I\niORdjit7mzZtokGDBhw4cIDy5cs74hHGrV27lq+++opvv/2WCxcuEBoaSteuXQkNDeWuu+4yHc+l\npaens2HDBr777jvmzp1LXFwcTZs2pUePHjzyyCPky5fPdMRsFRwcTJs2bXj33XdNRxG5LcnJycyY\nMYOpU6eyZcsWAgMDefrpp3n44Ydz9Q9qblVsbCwLFixg2rRpREdHU7duXXr37s2TTz6Z6/5dExER\nl+e4srds2TLat29PQkJCrtqmPzU1lblz5zJu3Dh27NhBtWrV6N69O0899RTFihUzHS9HstlsRERE\nMH36dL777jusVitPPfUUgwcPzjXfRLZs2ZIqVarwySefmI4icksSExP54osvGDt2LGfOnKFTp070\n6dOHli1b5uh1xs6wZcsWPvvsM2bNmoWPjw/9+vVjwIABOu9PREScZbfDFo4lJibi5uaWaw6RPn/+\nPCNHjqRMmTL07t2b2rVrs2XLFnbt2sUrr7yioncHrFYrrVq1Yvr06cTGxvLKK6+wYMECKlWqRLdu\n3di9e7fpiHesYMGCnD171nQMkZt2/vx5hg8fTunSpRkxYgRPP/00hw8f5ptvvqFVq1YqejchODiY\nTz/9lIMHD/Lss88yYcIEypUrx/Dhwzl//rzpeCIikgc4rOwlJSXh6+ub478hSE1NZeLEiVSsWJFx\n48bRp08fYmNjmT59utZiOEBAQACvvfYaBw8e5Msvv2TXrl3UrFmTXr16ZR3vkBOp7ElOYbfbmTFj\nBlWrVmXy5MkMHTqUQ4cOMWrUKIoUKWI6Xo5UpEgRRo0axaFDhxg6dCiTJ0+matWqzJgxAwduiC0i\nIuK4snf+/Pkcvz5h2bJlBAYG8vLLL/Pkk0+yf/9+Ro4cSYkSJUxHy/U8PT154okn2LZtG1988QWr\nVq2iSpUqvPnmm6SmppqOd8tU9iQniImJoUmTJvTs2ZNOnToRExPDq6++qvPksomfnx+vvvoqMTEx\ndOrUiZ49e9KkSRNiYmJMRxMRkVzKoSN7ObXsxcfH89RTT9G+fXvq16/Pnj17eP/99ylcuLDpaHmO\nm5sb3bt3Jzo6mlGjRjFu3DiCg4PZtGmT6Wi3xN/fn/j4eNMxRK5p2rRpBAcHk5aWxpYtW/jkk08I\nCAgwHStXCggI4JNPPmHz5s2kpaURHBzMtGnTTMcSEZFcSGXvH9atW0f16tX56aefWLRoEXPnzuXu\nu+82HSvP8/LyYvDgwezcuZOiRYvSqFEj3nrrrRwzBcrf359z586ZjiHyLxcuXKBbt2706dOH/v37\n88svv1C7dm3TsfKEoKAgfv75Z/r160efPn3o1q0bFy5cMB1LRERyEYeWvZy2Ocu0adNo3bo1jRo1\nYteuXXTu3Nl0JPmH8uXLEx4ezoQJExg9ejSPPPIIycnJpmPdkLe33GuyeAAAIABJREFUNxcvXjQd\nQ+QK586dIzQ0lBUrVrBy5UreffddPDw8TMfKUzw9PRkzZgwrV65kxYoVhIaGasq3iIhkG4eVvZSU\nFLy9vR11+2w3dOhQevfuzcsvv8yCBQu0NbYLs1gs9OvXj5UrV7JmzRqaNm1KXFyc6VjX5eHhkSPX\nGkrudebMGZo1a8b+/ftZv349LVq0+D/27jyuxvT/H/jrnPbSqkiWsqSkSMjWguxJiMgSspRlJuvw\nsU2yfAfDyJIM1aQFJSRr2ZKdqERIRDKUpaLScrp/f5j6jSFr51xneT8fjx7p3Kf7ep3jPnfnfa7r\nvi7WkWRaz549kZiYiMzMTNjb24v9OY0QQohkEFqxB0BiZuJcuXIl1q5di5CQECxfvlxicss6e3t7\nXL58Ga9fv4ajo6NYT2WuqKiI8vJy1jEIAfB+lmEXFxcUFBTg/PnzaN26NetIBEDr1q1x4cIFFBYW\nwsXFhT4gIoQQ8sOEWuxJgu3bt2PJkiXYsmULxo4dyzoO+UbNmjXD8ePH8fDhQ7i4uKCiooJ1pE+q\nKvYk5RpDIt2mTp2KpKQkxMbG0jXJYqZx48aIjY3FjRs3MHXqVNZxCCGESDiZLvbu3buHn3/+GUuX\nLoWnpyfrOELh6uoKOTk5zJ07l3UUoTE2NsbRo0eRmJiIdevWsY7zSQoKCuA4TmyLUSI7jh49iqCg\nIISHh8PCwoJ1HPIJ5ubmCA8PR1BQEI4cOcI6DiGEEAkms8Uex3GYOnUqTExMsGjRIpG2feLECWze\nvLnGn2tTZGQkbG1thbJvcWJlZYUlS5bg119/xZ07d1jH+YiioiIA0LAswlRZWRlmzZqF4cOHw8nJ\nSaRti/K8J2rCeCwDBw6Eq6srvL29aXInQggh301mi72YmBgkJCQgODhY5LPPbd++/bM/k+8zb948\nmJqaYunSpayjfISKPSIOdu7cicePH+P3338Xedusz3sVFRUICgrC/fv3a33fwnos69atw9OnTxES\nEiKU/RNCCJF+MlvsVS2z0K5du1rdr4eHB/h8fvWbqQ0bNkBNTQ0dOnQAAEycOBFRUVH46aefwOPx\n4Ojo+MHPz549w4MHD9C3b19oamqiadOm1YvtLlq0CHJycli4cCFWrVqFpk2bIiUl5atyPXjwAKNH\nj0bdunVhYGDwwdCgdevWoWHDhlBUVIShoSGioqIAAD4+PuDz+ejZsyeaN28ONTU1dO/evfrN0pe2\njxgxAjweD2pqatXDKydNmgRFRUV07NixFp7tD8nLy2P27NmIiYnBixcvan3/P6Kq2KNJWghLYWFh\nGDx4cK1fpyeO571bt26hV69e0NTUhLq6Oho1aoRr166hRYsWNe6zpgwAatz238f27NmzWnteGzVq\nhMGDByM8PLzW9kkIIUTGcEIyffp0zs7OTli7/yHPnj3j5OXluT179ghl/yYmJtzatWurf16+fDnX\nvn376p8NDQ25TZs2ffLniooKrnnz5ty0adO4goIC7vjx4xyPx+MuX77McRzH2dvbcw4ODlxCQgL3\n+++/czdu3PhiHnt7e87T05MrKiriioqKuKFDh3IdOnSo3j5z5kwuJSWFKykp4ebMmcMZGhpWb2vb\nti03ffp07u3bt1xOTg7n4ODAWVhYfPX2li1bcu7u7h/ksbW15UpKSr6Y+3sUFRVxmpqaHzy/4uD8\n+fMcAC47O5t1FCKjcnJyOD6fzx08eFAo+xe3816bNm04Nzc3rqCggHv8+DHXunVrzsPDo3r7f/d5\n7dq1GjN8Kd9/H1ttOnToEMfn87knT54IZf+EEEKk2m2h9eypqqqipKREWLv/IZcuXUJlZSX69+/P\nOspHrly5gszMTCxcuBAaGhro06cPWrRogRMnTlTfx9LSEnZ2dpgzZw4sLS2/ar916tSBqqoqVFVV\n0bdvXzx58qR62x9//IE2bdpAWVkZnTt3xvPnzz/4XX19faipqcHAwADLly/HzZs3cePGja/aPmnS\nJERHR1cvi5CSkgJzc3OhrcGoqqqK7t27IzExUSj7/140jJOwlpaWhsrKStjZ2bGO8pHaPu9VVFTg\n5s2bcHZ2hoaGBho3bgwbG5uPhnD+e59lZWU1ZviafMJiZ2eHyspKpKWlCb0tQggh0keoxV5RUZGw\ndv9DsrKyoK+vD3V1ddZRPpKVlQXg/fAdHo8HHo+HjIwMvH79utba4PP5HywBsHbtWrRs2RJqampw\ndXX97PIAJiYmAICHDx9+1fbx48ejrKwMe/fuBfB+GJmHh0etPI6aGBsb15iPFSr2CGvZ2dlQV1eH\npqYm6ygfqe3znry8PIyMjBATE4PCwkJkZ2cjISHhs8P2P5dBFOflmqirq0NLSwuPHz8WeluEEEKk\nj7ywdqyqqori4mJh7f6HlJSUQEVFhXWMT6oqCgoKCqChoSH09s6cOYP58+cjMjISffr0wbFjx+Du\n7l7j/auuOZOX//Sh89/tenp6cHZ2RkhICNzd3ZGWloa1a9fW8qP4kDj2KldNAkRLLxBWCgoKoKWl\nxTrGJwnjvBcaGooBAwZAV1cXmpqa6N+/P3x9fb8rQ3R0dK3n+xba2toiKSwJIYRIH6H17KmpqYlt\nsaelpYVXr14Jbf88Hg+VlZXf9btVEyekp6fXZqQapaenw8DAAMOGDYOGhgb4/M8fElVDiYyNjb96\n++TJk6tnPu3Tp08tJa/Zy5cvoa2tLfR2vsfnek0JESYDAwM8e/YMAoFAKPsXp/Mex3Hw9/fHypUr\nUVZWhry8POzcufOzhdrnMoj6vPxvHMfh6dOnaNiwocjbJoQQIvmEOoxTXIs9MzMzvH79Go8ePRLK\n/hs0aICTJ0/i7du3yM/Px99///3BdhUVFVy9ehWlpaV49+7dBz+bm5vD1NQUs2fPRlZWFgQCAZ4+\nfYo3b94IJauBgQHy8vKQnp6OV69e4dq1ax/dp6ioCOXl5Xj8+DGWLFkCe3t7tGrV6qu39+7dG4aG\nhpg7dy7GjBkjlMfxb8nJyTAzMxN6O4RIksaNG6O8vBw5OTlC2b84nfd4PB6UlZWrZ8jk8XhQUlKC\njY1Njef9Dh061Jjhc9s+9dhqU05ODkpLS2t9BlVCCCEyQlhTv+zZs4fj8/lcZWWlsJr4bqWlpZyu\nri7322+/CWX/x44d4+rXr89paGhwzs7OnJubG8fn87mffvqJ4ziOW79+PaeiosI1adKEO3fu3Ec/\n379/n+vevTunrKzMaWpqcq6urtyzZ8+4RYsWcXJycpySktIHs8p9TtXvqKiocAEBAVx8fDynra3N\nAeDGjBnDvXv3jhs4cCCnoqLCtW3blvP19eUAcH369OE47v1sm9ra2pyioiKnoqLCDR48mMvLy6ve\n/5e2V1m+fDk3ZMiQWnh2Py8rK4vj8/lcbGys0Nv6Frdv3+YAcKmpqayjEBlVUlIi1Jlqxem8x3Ec\nN2HCBC45Obn655KSEm7AgAGcl5dXjfusKcOXtv33sdSmzZs3c+rq6lxRUVGt7pcQQohMuM3jOOGM\nKzt+/Dj69evH7BqHL/H29sbx48eRnp4OHo/HOo7YsrS0hKurKxYuXPhd26usXr0alpaW6Nu3rzBi\nVvPx8cGff/6Jx48f13hdIQvp6ekwMzNDamoqLCwsWMchMmr8+PG4e/cuLl68yDqKUF2+fBkeHh64\ndevWB7dPmDABPB4PQUFBjJJ9u27duqF58+bYuXMn6yiEEEIkT7rQhnHq6ekBAPLy8oTVxA/x9PRE\nZmYmtm/fzjrKd8vKyqoeolTTV9Uscj/iS9fh1LQ9ODgYhYWFOHbsGM6ePSv0Qi8nJwcbNmzAlClT\nxKrQI0RcTJw4EZcuXcLx48dZR/luX3Pee/78OR4+fIhTp06htLQUeXl5CAgIQEREBFxdXVk/hK8W\nFxeHCxcuYOLEiayjEEIIkVBCe0f872KvefPmwmrmu5mZmWH27NmYN28eHB0dJfLidyMjI6FO+DF9\n+nSkpqbi3r17ePbsGTZv3vxN2y9fvgxPT09069YNYWFhQstZZcaMGahfvz7mz58v9LYIkUS2trZw\ncXHBjBkzkJaWBiUlJdaRvtnXnvfWr1+PadOm4dGjR5CXl0e7du0QHR2Nfv36iSDljysrK4O3tzdc\nXFxgb2/POg4hhBAJJbRhnFUX4B88eBBOTk7CaOKHlZSUoE2bNqhfvz7i4uKgqqrKOhL5TmvXrsWC\nBQtw6tQpsXxjRMM4ibh4/PgxzMzMMGHCBGzatIl1HFIDb29vBAYG4tatWzA0NGQdhxBCiGQS3jBO\nZWVl1KlTR2yHcQLvZ1A7cuQIMjIy4OrqSmugSaiwsDDMnz8f69atE8tCjxBx0qRJE+zcuRP+/v7w\n9/dnHYd8QmBgIDZt2oTt27dToUcIIeSHCK3YAwBdXV2xLvaA9+vBxcbGIiEhAcOHD0dRURHrSOQb\nbN++HRMmTMDChQsxc+ZM1nEIkQhDhw6Fr68vvL29ERERwToO+ZeIiAh4eXlh2bJlcHNzYx2HEEKI\nhBNqsaenpyf2xR4AWFtb49ixYzh//jxsbGyQnZ3NOhL5goqKCnh7e8PT0xOLFi3C8uXLWUciRKIs\nXLgQs2fPxpgxY7Bu3TrWcQjeX2c4ZswYzJw5E4sXL2YdhxBCiBQQarFnYGAgtAV8a1u3bt1w5coV\nCAQCdOzYEQcOHGAdidTgwYMH6N27N3bs2IE9e/bAx8eHls8g5BvxeDysXr0a69evxy+//AJPT08U\nFxezjiWTiouL4enpiXnz5mH9+vVYu3YtndMIIYTUCqEWe4aGhnj8+LEwm6hVRkZGOH/+PAYMGIAh\nQ4bAzc1NInomZUVlZSU2btyINm3a4OXLlzh//jyGDx/OOhYhEm3mzJnYu3cvoqKi0KFDB9y4cYN1\nJJly48YNdOjQAXv37kV0dDQNRyeEEFKrhFrsNWnSRKKKPQBQV1dHUFAQjh49ivPnz8PMzAwbN25E\nWVkZ62gyLSEhAV27dsXcuXMxZ84cXLt2DZaWlqxjESIVhgwZgpSUFNSvXx9dunTBokWL6PplISsq\nKsKiRYvQpUsX1K9fHykpKRg8eDDrWIQQQqSM0Iu9p0+fSmSh1K9fP6SlpWH8+PGYP38+TExMEBYW\n9sUFxkntSklJwYABA9C9e3doaGjg2rVrWLZsGRQVFVlHI0SqNG7cGCdPnsRvv/0Gf39/mJiYIDw8\nXKhrecoijuMQFhYGY2NjrFmzBi4uLjh48CAaNWrEOhohhBApJPRhnJWVlRJz3d5/aWhoYO3atcjI\nyECfPn0wYcIEtGzZEn5+fvSpt5CdO3cOrq6uaN++PfLy8nDixAnExcWhTZs2rKMRIrX4fD5mzpyJ\nzMxMuLi4YNy4cWjbti127txJS9P8oMrKSsTGxsLa2hrjxo1D586d0a9fP+zduxcNGzbEpEmTcP78\nedYxCSGESBmh9+wBkLihnP/VqFEjbNu2DTdv3oSDgwP+97//oXHjxpg/fz4yMzNZx5Mab9++RVBQ\nENq2bQtbW1vk5uYiOjoaV65cgYODA+t4hMgMHR0d+Pn54caNG9ULsLdu3RqBgYE0ics3KikpQWBg\nIFq1aoXBgwejefPmSE5Oxr59+xAbG4vnz5/j999/R3JyMmxsbGBiYgIfHx9kZWWxjk4IIUQKCLXY\nq1+/PpSVlfHo0SNhNiMypqam2LZtG7KzszF37lyEh4fD2NgYNjY2CAgIwKtXr1hHlDgCgQDHjh3D\nmDFjoK+vj2nTpsHS0hLXr1/HmTNn4OzsTLPSEcKIhYUFdu/ejfT0dNjY2GDatGkwMDDA9OnTkZyc\nzDqeWEtOTsb06dNhYGCAadOmoVu3bkhPT8fu3bthYWFRfT8tLS1MmTIF165dQ1paGoYMGQJ/f380\nb94cvXv3xs6dO1FSUsLwkRBCCJFkPE7IF2SYm5tjyJAhUrkOmkAgQHx8PMLCwrB//34IBAL07t0b\nTk5OcHR0RMOGDVlHFEslJSU4efIkDh06hJiYGDx//hxdu3bFmDFj4OrqCh0dHdYRa116ejrMzMyQ\nmpr6wRs9QiRJbm4uQkJCsGPHDty7dw9WVlZwdXWFi4sLWrRowToecxkZGYiOjkZUVBSuX7+Oli1b\nYtKkSRg3bhzq1av31fspLS3FwYMHERwcjLi4OGhpaWH06NEYP3482rVrJ8RHQAghRMqkC73YGzZs\nGHg8HqKiooTZDHNv3rzB/v37sW/fPpw4cQLFxcVo164dBg4cCAcHB1hbW0NZWZl1TGbS09ORkJCA\nI0eO4OTJkygpKUH79u3h7OwMNzc3NG/enHVEoaJij0gTjuNw9uxZhIaGIiYmBi9evECbNm3g4uKC\nvn37okOHDpCTk2MdU+gEAgGuXbuG48ePIzo6GqmpqdDV1YWzszPGjh0LOzu7Hx6Z8PTpU0RFRSEw\nMBA3b95E+/btMWXKFIwePRpqamq19EgIIYRIKeEXe4sXL0ZMTAxu3rwpzGbEyrt373Dq1CkcOnQI\nhw8fxuPHj6GkpARra2vY29vDxsYGHTt2lMoeLAAoKytDamoqzp8/j7Nnz+LcuXPIzc1FnTp14ODg\ngIEDB8LR0RENGjRgHVVkqNgj0kogEODMmTPYt28fYmJikJOTAy0tLXTv3h0ODg7o3r07WrVqJRXF\nn0AgQHp6Ok6fPo2TJ08iISEB+fn5aNiwIZydneHi4gJ7e3uhPFaO43Du3DkEBAQgOjoaKioqGDt2\nLLy8vGBmZlbr7RFCCJEKwi/2QkNDMWnSJBQVFUFeXl6YTYmthw8fIjExEQkJCUhMTERGRgaA94u4\nt2vXDu3atYOVlRXMzMzQpEkTiXpT9PLlS9y5cwfJycm4fv06bty4gbS0NJSXl0NbWxs2Njaws7OD\nnZ0drKysZPYYoGKPyIrbt2/j5MmTOHnyJM6cOYOCggLUqVMH7du3R8eOHWFtbQ1LS0s0bdpUrM8H\nAoEADx8+xI0bN3DlyhVcvXoVSUlJePv2LTQ1NauLWQcHB5EXW/n5+YiMjISfnx9u375d3ds3duxY\nqKioiDQLIYQQsSb8Yu/q1auwtrZGRkYGXdPxj+fPnyMpKQk3btyo/nrw4AEAQElJCc2bN0fLli1h\nbGyMZs2aoVGjRmjYsCEaNGiA+vXri3TCksLCQuTk5ODp06d4+vQpsrKycO/ePdy7dw8ZGRl4/fo1\nAEBbW/uDwrVdu3YwMTEBny/UOYAkBhV7RBYJBALcvHkTV69exZUrV3DlyhXcvn0bFRUVUFRURMuW\nLWFqagpTU1MYGxujcePGaNiwIRo3biySoqWkpATZ2dnIyclBdnY2MjIycOfOHdy5cwd3795FeXk5\n5OXlYWZmBmtr6+ovc3NzsfhQjuM4nDx5En/++ScOHDgANTU1uLu7w9vbG82aNWMdjxBCCHvCL/be\nvHkDTU1NHDx4EAMHDhRmUxItPz8fd+/eRUZGRnUhlZGRgQcPHlQXVACgoKCA+vXro379+tDW1oam\npiY0NTWhpaUFTU1NKCkpQVVVFUpKSgAARUXF6us6ysvL8fbtWwDv3yTk5+dXf3/9+jUKCgqqv169\neoUnT558sJ6gsrIymjRpAmNj4+pitOrL0NBQhM+W5KFij5D3ioqKkJ6ejjt37lR/v3z5MnJzc1Fe\nXl59P11dXRgYGEBHRwfa2trQ1taGlpYWtLW1oaCgAE1NzeoPk6r+XVlZiYKCAgCo/nd5eXn1Oa7q\n69WrV3j69ClevHhR3Z6ysjKaNm2KVq1aIT8/H9evX0dYWBi6d+8uEdfGPXv2DCEhIdi6dSuys7PR\ns2dPTJkyBUOGDBHrHlRCCCFCJfxiD3i/Tt3MmTMxd+5cYTcllUpKSvDkyRP8/fff1d/z8vLw+vVr\n5Ofno6CgoPp7WVkZ3rx5U70A8rt376qn7ZaTk4OGhkb1fqveIFUVjVUFo5aWFrS0tNC4cWPo6+tX\nf9fV1WXy+KUBFXuEfNrDhw9haWkJLy8vzJkzp7qX7fHjx3j27BlevXr1QaH2+vVrVFRUID8/v3of\n//5ATFtbu/rfWlpakJeXry4Wq750dHSgr6+PJk2aVPcm/nu2zDdv3sDGxgYCgQDnz5+HpqamaJ6M\nWiAQCHDo0CEEBAQgLi4OBgYGmDJlCjw9Pb9pRlBCCCFSQTTFXv/+/aGnp4edO3cKuynyCWZmZhgx\nYgR+/fVX1lFkFhV7hHyssrISPXv2xIsXL3Dt2jWxmrH40aNH6NSpE6ysrBAbGysWwza/1YMHD/Dn\nn38iMDAQb9++xahRo+Dt7Y02bdqwjkYIIUQ00kVyQZWlpSVu3LghiqbIJ1RUVNAwHkKI2Fm9ejUu\nXbqEiIgIsSr0AMDQ0BCHDh1CQkIC5s2bxzrOd2nWrBl+++03PHnyBNu2bcOVK1fQtm1b2NjYICoq\nCgKBgHVEQgghQiayYu/OnTt49+6dKJoj/yEQCCTyU2lCiPRKTk6Gj48PVqxYIbY9TR06dEBISAg2\nbNgAf39/1nG+m5KSEtzd3XHz5k0kJiZCW1sbI0aMgImJCVavXv3BkFhCCCHSRWTFXkVFBdLS0kTR\nHPkP6tkjhIiT0tJSjBs3DtbW1pg1axbrOJ81bNgw+Pj4wNvbG/Hx8azj/DAbGxvExsbi3r17cHR0\nxPLly2FoaAhvb288evSIdTxCCCG1TCTFnrGxMerUqUNDORmhnj1CiDhZuHAhsrKyEBoaKhHnpiVL\nlsDNzQ3Dhg2Tmg8tW7RoAT8/P+Tk5MDX1xcHDhxAs2bN4OTkhBMnTrCORwghpJaIpNjj8/lo06YN\nUlJSRNEc+Q/q2SOEiIvExERs2LABGzduhJGREes4X4XH42HHjh2wsrKCk5MTcnNzWUeqNZqamvD2\n9sb9+/cRFhaG3Nxc9O7dG506dcLu3bvpuj5CCJFwIlvxmiZpYaeiokIiPj0nhEi3goICjB07FoMG\nDcK4ceNYx/kmioqK2Lt3L+Tl5TF06FCUlpayjlSrFBQU4ObmhsuXL+P8+fMwNDTEmDFjYGJigj//\n/FPqHi8hhMgKkRV7HTp0wPXr11FWViaqJsk/BAIB9ewRQpibMWMGSkpKsG3bNtZRvkvdunURGxuL\nW7duYdy4cRDBykVMdO3aFZGRkbh//z4cHR0xc+ZMGBoawsfHp3rRekIIIZJBZMVely5d8O7dOxrK\nyQAN4ySEsLZ//36Eh4cjKChIohf3NjU1RWRkJKKjo7Fy5UrWcYTKyMgIfn5+yMrKgpeXFzZs2IDm\nzZvDx8cHr169Yh2PEELIVxBZsWdiYoK6devi4sWLomqS/IMmaCGEsPT06VNMnjwZnp6ecHR0ZB3n\nh/Xu3RsBAQFYunQpIiIiWMcRunr16sHHxweZmZmYMWMGNm3aVD2DZ05ODut4hBBCPkNkxR6Px4O1\ntTUuXbokqibJP6hnjxDCCsdxmDx5MjQ1NbFmzRrWcWrNxIkT8fPPP2PixIky8yFm3bp14ePjg0eP\nHmHFihWIjo5Gs2bN4O7ujoyMDNbxCCGEfILIij3g/VBOWfmjKE6oZ48Qwoq/vz/i4uIQHh4OdXV1\n1nFq1fr169GnTx8MGjQImZmZrOOITJ06deDt7Y0HDx5g+/btuHTpEkxNTeHq6orbt2+zjkcIIeRf\nRF7sZWVl4enTp6JsVqZVVlaisrKSevYIISKXmZmJBQsWYOHChejcuTPrOLWOz+cjPDwcDRs2xKBB\ng2Ru8hJFRUW4u7vj1q1bCAoKQlpaGiwsLDBs2DAkJyezjkcIIQQiLvasra3B5/Nx+fJlUTYr06rW\nSKKePUKIKFVUVGD06NFo2bIlFi9ezDqO0NSpUwdHjhxBQUEBRo4ciYqKCtaRRE5BQQHjxo1DWloa\nIiMj8fDhQ1hZWUnVIvSEECKpRFrsaWhowMLCAomJiaJsVqZVvfGgnj1CiCitWLECKSkpCAkJgYKC\nAus4QmVgYICYmBgkJiZizpw5rOMww+fz4eLigqSkJMTFxSErKwtt2rSBk5MTzcRNCCGMiLTYAwB7\ne3skJCSIulmZVdWzR8UeIURUkpKSsGrVKqxZswbm5uas44hE+/btERISgs2bN2PLli2s4zDXq1cv\nXL16FTExMXjy5AmsrKzg6uqKe/fusY5GCCEyReTFXvfu3ZGcnIzXr1+LummZVNWzR8M4CSGiUFxc\njNGjR8Pe3h4zZsxgHUekXFxc4OvrC29vbxw6dIh1HOZ4PB6cnJyQlJSE0NBQpKamonXr1pg0aRKy\ns7NZxyOEEJnApGcPAM6ePSvqpmUS9ewRQkRp3rx5eP78OYKCgsDj8VjHEblFixbBw8MDo0aNws2b\nN1nHEQt8Ph+jRo3CrVu3EBgYiJMnT6Jly5aYN28eXr58yToeIYRINZEXezo6OrCwsKChnCJCPXuE\nEFGJj4/H1q1bsXXrVjRu3Jh1HGa2bNmCDh06YNCgQXj+/DnrOGJDTk4O7u7uuHfvHvz8/BAWFgYj\nIyMsWLAAb968YR2PEEKkksiLPeD9UM4zZ86waFrm0AQthBBRyM/Px8SJE+Hm5oaRI0eyjsOUgoIC\noqKioKCgACcnJxQXF7OOJFYUFBQwZcoU3L9/H4sXL0ZAQACaN2+O1atXo7S0lHU8QgiRKsyKvZSU\nFBq+IQK09AIhRBS8vLxQWVmJTZs2sY4iFurWrYvY2Fjcv38f48ePB8dxrCOJHTU1NcyfPx8ZGRlw\nc3PD0qVLYWFhgf3797OORgghUoNJsWdnZwcej0dDOUWAevYIIcIWGhqKyMhI7NixAzo6OqzjiA0T\nExMcOHAAMTEx8PX1ZR1HbOnp6cHPzw93795Fhw4d4OLiAnt7eyQlJbGORgghEo9Jsaejo4P27dsj\nLi6ORfMyhSZoIYQIU05ODry9veHt7Y1+/fqxjiN27OzssHXrVixbtgxhYWGs44g1IyMjRERE4PLl\ny+A4Dh07doSrqyuysrJYRyOEEInFpNgDgL59++LYsWOsmpdKVFvXAAAgAElEQVQZNEELIURYKisr\n4e7uDn19faxatYp1HLHl4eGBWbNmYfLkybhw4QLrOGKvY8eOOHv2LGJiYnD9+nW0bt0aCxYsQGFh\nIetohBAicZgWe48ePcLdu3dZRZAJ1LNHCBGWDRs24OzZswgJCYGKigrrOGJt7dq16Nu3L5ydnZGZ\nmck6jkRwcnLCrVu34OPjg23btsHU1BShoaF0/SMhhHwDZsVe586doaOjg+PHj7OKIBOoZ48QIgy3\nb9/G4sWL4ePjg44dO7KOI/b4fD4iIiLQtGlTODk5IT8/n3UkiaCkpIR58+YhIyMDzs7OGD9+PGxt\nbZGcnMw6GiGESARmxZ6cnBx69OhBxZ6Q0QQthJDaVl5ejnHjxsHS0hILFixgHUdiqKqq4sCBA3jz\n5g1GjhxZfX4mX6arq4utW7fi2rVrAID27dvD3d0deXl5jJMRQoh4Y1bsAe+Hcp45c4bW1REiWnqB\nEFLbli5divT0dISEhNC55RsZGBggJiYG586dw9SpU1nHkTjt2rVDYmIigoODER8fD1NTU/j5+VX/\nrSOEEPIh5sVecXExEhMTWcaQatSzRwipTRcuXMDatWvxxx9/wNjYmHUciWRlZYWdO3ciKCiI1iX8\nDjweD+7u7rhz5w7c3d0xd+5cdOrUqbrXjxBCyP/HtNhr0qQJzM3NcejQIZYxpBpN0EIIqS1FRUUY\nP348evXqhUmTJrGOI9GGDh2KlStXYtasWYiNjWUdRyJpamrijz/+QHJyMurUqYPOnTtj5syZePPm\nDetohBAiNpgWe8D72bZiYmJYx5BaNEELIaS2eHt74/Xr1/jrr7/A4/FYx5F4CxYswMSJEzF69Gik\npqayjiOxWrdujdOnTyMoKAgREREwMTFBVFQU61iEECIWxKLYy8rKwq1bt1hHkUrUs0cIqQ0HDx5E\nYGAgtm7dCn19fdZxpMbmzZvRsWNHDBo0CM+fP2cdR2L9e2ink5MTRowYAScnJzx+/Jh1NEIIYYp5\nsdepUyfo6+vTMJZa8PbtW5w4caL669SpU7hz5w4A4ObNm0hKSkJKSgoePHiAhw8fMk5LCBE3hYWF\nePHixUe35+XlYcqUKfDw8MCwYcMYJJNeCgoKiIqKgpKSEgYOHIji4uJP3q+oqEjEySSTjo4Otm3b\nhvj4eNy9exfm5ubYuHEjKisrWUcjhBAmeJwYrE7q4eGBu3fv4vz586yjSLSysjLUq1cPBQUFX7yv\nnZ0dEhISRJCKAEB6ejrMzMyQmpoKCwsL1nEI+aTIyEj89NNPCA4OxoABA6pvHzZsGK5fv47k5GRo\naGgwTCi9Hjx4gE6dOqF79+6IjIz8YJhsUFAQAgMD6W/kN3r37h1WrlyJNWvWoGPHjggKCkLLli1Z\nxyKEEFFKZ96zB7wfynnp0iUawvKDFBUVMXjwYCgoKHz2flXDXQgh5N+OHz+OvLw8ODo6wsvLC0VF\nRdixYwf279+P4OBgKvSEqFmzZti3bx9iY2Px66+/AgAqKysxf/58TJw4ERcuXEBaWhrjlJJFWVkZ\ny5cvR1JSEkpLS9G2bVusXr2almkghMgUsSj2+vTpA0VFRRw9epR1FInn6uqK8vLyz95HXl4eLi4u\nIkpECJEUhw8fRtVgj8DAQBgbG8Pb2xtz586Fvb0943TSz9bWFgEBAVixYgWCgoLg5uaG33//HcD7\n4Z7BwcGME0omc3NzXLx4ET4+Pvj1119hY2NTfYkDIYRIO7Eo9tTU1NCjRw+albMW9O7dG+rq6jVu\nl5eXx5AhQ6ClpSXCVIQQcXfr1q0PRldUVFQgNzcXJSUlEAgEKCsrY5hOdowfPx5Tp07F3LlzsX//\n/uprzcrLyxEYGIjS0lLGCSWTvLw85s+fj6SkJAgEArRr1456+QghMkEsij3g/ZpDx44do/VxfpCC\nggJcXFxqHMpZUVGBcePGiTgVIUTcxcXFfTRrr0AgAMdx2LBhAywtLWl5ABFITU3F/v378fbt249G\naRQWFtK6tD+odevWuHDhAhYvXoylS5fC1tYWmZmZrGMRQojQiE2xN3jwYFRUVODYsWOso0i8zw3l\n1NbWRu/evUWciBAi7o4cOVLjjIUCgQAZGRno2LEjNm3aBDGY10sqHT16FF26dEFeXt4nz+F8Ph/b\nt29nkEy6yMvLY9GiRUhKSkJxcTHatWtHQ2QJIVJLbIo9XV1d2NraIjo6mnUUiderV69PDuVUVFTE\n+PHjvziBCyFEtrx79w5nz5794vT0lZWVX7wmmHyfdevWwdHRESUlJaioqPjkfQQCAeLj45GTkyPi\ndNLJ3NwcV69exezZszFp0iS4uLjg5cuXrGMRQkitEptiDwBcXFxw+PBhlJSUsI4i0RQUFDBs2DAo\nKip+cHtZWRnGjh3LKBUhRFydPXv2s9fkKSgoQF9fH4mJiZg9e/YHywKQ2mFoaIgGDRpATk7us/eT\nk5PDzp07RZRK+ikoKMDHxwfx8fG4cuUKzM3NabI4QohUEbtir7i4GPHx8ayjSDxXV9eP3rwZGxuj\nXbt2jBIRQsRVXFzcRx8OVeHxeHB1dUV6ejo6d+4s4mSyY9iwYbh//z5WrFgBZWXlGkdglJeXY9u2\nbTSUtpb17NkTycnJ6NatGwYOHIh58+bRZDiEEKkgVsWevr4+OnfujH379rGOIvEcHBw+WBNLQUEB\nkyZNYpiIECKuYmNjP/pwSF5eHqqqqti5cyfCwsJQp04dRulkh4qKCubPn4+MjAy4uroCwEeT5gDA\no0ePcO7cOVHHk3p169bF3r17ERwcjICAAHTt2hUPHjxgHYsQQn6IWBV7wPtZOWNiYmia7x9UNZSz\n6tPhiooKuLm5MU5FCBE3OTk5yMjI+OA2OTk5mJubIyUlBWPGjGGUTHY1atQIYWFhOHXqFFq0aAE+\n/8M/1QoKCtixYwejdNLP3d0d169fr16iYe/evawjEULIdxO7Ys/FxQUFBQU4ceIE6ygSr2pWTj6f\nD3t7ezRu3Jh1JEKImImLi6u+Bo/P54PH42HatGm4fPkyWrRowTidbOvRowdu3ryJrVu3QkNDo/rD\nu/LyckRGRqKwsJBxQullbGyMS5cuYfz48XB1dYW3tzdNTkQIkUhiV+wZGRmhS5cu2LVrF+soEs/B\nwQFaWlqorKyEh4cH6ziEEDF0/PhxAO97i+rWrYv4+Hhs3Lixxmv4iGjJy8tjypQpyMjIwOjRo8Hj\n8SAvL493794hMjKSdTyppqysDD8/P4SEhCAwMBA2NjZ49OgR61iEEPJNPr4YQAy4ublhwYIFKCoq\ngpqamkjbjouLQ35+vkjbFCYrKyucO3cOHMdJzRsDMzMzmJubs45BJNzt27eRlpbGOgZTHMchNjYW\nlZWVaNOmDaZPn46XL1+K/FzRt29faGpqirTNLxHH46N///5o2bIlduzYgQcPHuC333774Nps8n2+\ndPyNHTsWlpaWGD58OKytrREeHo5evXqJMCEhhPwATgzl5uZy8vLy3K5du0TeduvWrTkA9CXGX4sX\nLxb5cfGjbt++zQHgUlNTWUch/1i2bBnzY5m+ILavCzo+ZOfra4+/wsJCbuTIkZycnBy3Zs0aIR+B\nhBBSK26LZc+enp4eevXqhV27dmHkyJEibZsDMGzqTLh5/yLSdoVFIKjAw9tpaGFhyTpKrZgzqCfr\nCESKNG7WAhuOnGUdg5lLcYeh36QpjEzNmLT/JDMD3o72TNr+GuJ+fBS/KUTe0xwYmrRiHUUifevx\np66ujl27dqFHjx6YMWMGkpKSEBgYKPIRSIQQ8i3E7pq9Km5ubjh69ChevnzJOopEk5OTl5pCjxBS\nuzr3cWRW6JEfp6quQYUeA1OmTMGJEydw+vRpdOvWDQ8fPmQdiRBCaiS2xZ6LiwsUFRURHR3NOgoh\nhBBCSDU7Oztcu3YN8vLy6NixI80gTggRW2Jb7KmpqWHgwIE0KychhBBCxE7jxo2RkJAABwcH9O/f\nHxs3bmQdiRBCPiK2xR7wfihnQkICTXVMCCGEELGjpqaG3bt3Y/ny5Zg1axamTJlC6/ERQsSKWBd7\n/fv3h46OjtQsGUAIIYQQ6cLj8bBgwQIcOnQIe/bsQc+ePZGXl8c6FiGEABDzYk9RURFDhw6loZyE\nEEIIEWv9+/fHuXPnkJ2djS5duiA9PZ11JEIIEe9iD3g/lPPGjRu4desW6yiEEEIIITWysLDAtWvX\nYGBggG7duuHkyZOsIxFCZJzYF3v29vZo1KgR9uzZwzoKIYQQQshn6erqIi4uDn379kW/fv0QEBDA\nOhIhRIaJfbHH5/MxYsQIREREgOM41nEIIYQQQj5LWVkZERERWLx4MaZNm4YFCxbQexhCCBNiX+wB\nwKhRo5CZmYkrV66wjkIIIYQQ8kU8Hg+//vorQkJCsH79eri7u6OsrIx1LEKIjJGIYs/KygpmZmY0\nUQshhBBCJMrYsWNx9OhRHDx4EP3790dBQQHrSIQQGSIRxR4AjBw5Ert27aL1awghhBAiURwcHHDu\n3DncvXsXtra2ePLkCetIhBAZITHF3oQJE/Dy5UscOXKEdRRCCCGEkG9iYWGBc+fOoby8HLa2trhz\n5w7rSIQQGSAxxV6jRo3Qo0cPBAcHs45CCCGEEPLNjIyMcP78eTRq1Ahdu3ZFYmIi60iEECknMcUe\n8L537/Dhw3j27BnrKIQQQggh30xHRwcnTpxAr1690Lt3b0RFRbGORAiRYhJV7A0dOhTq6uoICwtj\nHUVs7N++BWPat8TEbm1rbZ/nDh/AVIdOGNnGCDcST3/yttDfV2DJ2KG11iYh5POE8Von0kPUx8e6\nmZ4YZtYIIat9RdKetFFSUsKuXbvg4eEBNzc3+Pv7s45ECJFS8qwDfAtlZWWMGDECO3bswNy5c1nH\nEQtDJk+HupY2dm1YXSv7e1tYgM0LZ2HZX1EoelOId8VFn7yNECJatf1ar22pFxKR8/A++o+ewDqK\nTBL18TFnwzYsHesikraklZycHPz9/WFiYoIZM2bg7t272LBhA3g8HutohBApIlHFHvB+KGdAQAAu\nX76MTp06sY4jFmrzD8PfWQ9QXlqKRs1bQk1DAwCQkXrjo9u69B1Ya20SQr7Oj77WBYIKJBzYC7OO\nnaHfxKh2Qv0jPiocZh3onMwSFQmSydvbG9ra2pg0aRLy8/OxY8cOKCgosI5FCJESElfsWVtbo02b\nNggODhaLYm/3pt8RHeAHDe264PN5KHz9Cnw5OTRqZozit29Q8PIFBIIK2A9ygZfvWgBA3tMn2OG7\nCGlXLkBRWRlmHTpj0pIV0Nar/1Xb81/mIWDJPKReSEQlVwnVOurg4ev/yB8MDkBs8DYUvHoFbb16\nGDd/Kbr2c8KFY7HY7rsQAOBubQoAmLXOH4Erl3xw2+z1AQj49Rcoq6pie8J1RPzxG/Zt34whk6ZD\nWU0NJyLDYWRqhmtnTnzT80KIOJPE13r2/bsIXLkE92+mgKushLKKKjr1GYCeLiM/+bqdvyUYKmpq\n2OazAPdSrkNdUwvDps6EwzA3ADWfO7Ysmo2Lx2Jx4ehB7Fi+CIHnkqGlW6+W/wfEm6QdH1sWzsLp\n/ZEYO28xnD2m4lDIdkT88RsaNTfGmuhjiA7ww+5Nv6Nrv4Hg8+VwPeEkFFVU8NP/+aFNV9vq/Tx/\n8ggb5k7HjcTTUFBUwrQVv8PK3gFAzcfLns3rELVlPVpbd0Xe0yfIz8tFCwtLTFu5DvpNjL64fd0s\nL1w4ehBKyioY6T0PgyZ4wX/xHJw5sBdGpmZYs/dobf7XioS7uzvq1auH4cOHIzc3F9HR0VBVVWUd\nixAiBSTqmr0q7u7u2L17N4qLi1lHwcif5qK1dRe06WKDPw6dwc4r6bC06Y7it2+wZEcE/rp0CzNW\nbUB8ZDj+fvQQALBulhd4fB62xJ3H2uhjKHpTgNUzJlbv80vbV3m6403+a/gdSUD49QwMmTzjmzK/\nfPY3Fm+PQHjSPXTt54Sda5YDALr2c8IvmwIBADuv3EH0naewcRz80W3dBgzCmDkLq/c3atYCmLXv\nhIybN9DKyhr9x0yA64xvf14IEWeS+Fr/Y840aOroYvuZa/A7nAB1bR2Ul5UC+PTrViCogO9ENzQw\nbIrtZ67By3cNti6Zi4zUGwBqPndMX7keug0aYtKSlYi+81TmCj1A8o6P6av+QAOjZtU/Dxw3GUM9\nf6r+2cXLG62srKFaRwNey9di25lrMGzZCpFb1n2wH00dXUxd8Tv+TEiCiWV77N70e/W2mo6XETPm\nwNCkFRq3aIk/Dp7E5rgL4MvLYc0/j+1L2+f8EQADo2bo0m8gBk3wAgBMW7EOLS2tsDL8wFc/B+Km\nX79+OH36NJKSktCvXz8UFhayjkQIkQISWeyNHTsWxcXF2L9/P+so1dS1dVBHQxNKKqqwsuuJd8XF\n0G9iBHkFBVjZ9QAAvMp9hoe305CRch1uM+dDS7cedBs0xOhZ/0NGynU8vJ32xe2ZaSnITEuB+7wl\n0DNoBDk5eaio1fmmrBP+twyGJq2goKSElpZWyH+ZVyvPQVNTc5h17IxBE7zQtFXrb3peCJEUkvJa\nFwgq8PjeHVg79INKHXXoNjBAq/bWePY464P7/ft1W1FejmePs+Di+TNU6qijbTd76DcxQurF99PD\nC+vcIU0k5fj4WsqqalBSVoGyqhra2fXEy2d/f3K7krIKLG27f7D9S8eLlq4elFRUoVOvPty85+PR\nvXQ8vJ32Vdt7DR+Fi8cPV19DnnXnNpoYm0JBSanWnwNR6tChA86ePYsHDx6gZ8+eePHiBetIhBAJ\nJ3HDOAGgXr16cHR0RHBwMEaPHs06zkd4PD7Acf//Z/4/NTXH4Vl2FgBAv7Fh9fZGzVoAQPW2z22v\nKC8HABiZmn13vphAf8RHhuPV82coK30HeRFdG/C554UQSSTOr3U5OXnoNWyMKyePwcquB4revMGt\nqxdhadO9xt/Jy8kGAEy2t/rg9qKCAgDszh2SSpyPj+/B5/PBfeZ8/d/H+y3HS8OmzQEAz3Meo6mZ\n+Re39xgyAuF/rMbF44fQY8gInD0YDQeXkd/70MSKqakpzp07h169esHe3h7x8fEwMDBgHYsQIqEk\nsmcPeD9Ry6lTp/DgwQPWUb7Lv/9gVla+//e/L66vaXtFeRkAQE7++95k3bpyAaG/r8To2f9D4Llk\nzF6/9bv2Qwj5Oqxe6wDgvWYTkhJOYlxnc8wb2gctLCzh9vO8Gu9f9WY87NpdRN95Wv3l/ssSOncI\nCcvjQ5i+9XipqHhfvMrJffoz6P9u19CpC2uHvji9PwpcZSUeZ9xBc3PpWZbEyMgIp0+fRkVFBXr2\n7Ins7GzWkQghEkpii70BAwZAX18fO3fuZB3lm+g3aQoAeJ79uPq2nIcZ1du+tF1XvyEA4O9H31fk\nPsnMgHa9+ujSdyBU6qj//0+aCSG1ivVrneM4HIv4C6NnLUBk2iMEX0zDz6s3QqWOeo2/o9vgfZtP\nMjM+2kbnjtrF+vgA3heNXKVwRlZ86/GSfe8uAKCBYdOv3t7bdTRuX72IU/v2oG03+1pKLj4aN26M\ns2fPQklJCba2trh//z7rSIQQCSSxf63l5eUxevRo/PXXX6isrGQd56s1bdUaLdu2xy6/1ch/kYsX\nfz9FxB+r0bJtexiZmn1xu1nHTqir3wAhq32rtyedif/q9rXr6aPw1Us8yczA24J8ZKalCPHREiK7\nWL/WeTweFJSUsGP5IriYGsDF1AAjLAyxaJQz8p4++eTvNDdvi4bNWiD4Nx/k5mSjUiDAq9znKCl6\n+8Vzh6KyMu7fTEZ5WRnKS0t/6LmTBayPDwDQ1quP1IuJeFdchKLCQrzOy621x/c1f2velRRDUFGO\nF3/nYJffarTu2AWNmht/9fY2Xe2gZ9AIIWuWwX6QdK75V79+fZw5cwb6+vqwtbVFWlral3+JEEL+\nRWKLPQCYNGkSHj16hNOnTzPLsGfzOty6chFxe0KxZ/M6nDt8ACFrfJH/Mg/zhvat/g4AG+bNwLPH\nWZi13h8CgQDTenXBzwPsoKisjFnr/av3+bntcvIK+N/WnSgqLMS03l2xYvJoqKprIv9lHv6YM+2L\nedvZ9oBlt+6Y59IXv44bBiUVVZSXlcF3ohvu30zGH3OmAgBmOzvgaHjwJ2+LDvDDzjXL8er5M8we\n5ICtS+Yi/foVHA0PwpZFs7/7eSFEnEnaax0AwHFYd+BE9XDMsGv3oKqugf1/bkbEhtUfvW55PB4W\nBuyEvIIifh5gh3GdWyP4/35F6buSz547AKC36xhcOBaLGX27IfNWai0+85JBEo+PIZOn49Gd25hk\nZ4XN//PG24J8PEhPQ+CKxYjethHp16/g2K6/ELcnFNcTTiJiw2rkPX2CrUvmVh8/VdtTLyQidO1y\n5L/Mg98vP33xeAGA+N2hGNWuBX7qbwtNXT3M3bj9g3xf2s7j8eAwzA3mnbpBQ6fud/2/SQJtbW0c\nP34czZs3h4ODA5KTk1lHIoRIEB73uautJUDnzp3RokULhIWF1cr+Wpubw8yuN9y8f6mV/ZHaNWdQ\nT4xxHYbly5ezjvJN0tPTYWZmhtTUVFhYWLCOQwD4+vpiR0goNhw5yzqKUGSkXMfmRbPhd+jMB7dv\n/t8s8Hjvp95n7UlmBrwd7cXydSHtxwdrcwb3Qtf+g+Di+fN3ba+yf/sWNG3V+rMTD9VEnI+/Tyku\nLsbgwYNx9epVHDlyBF26dGEdiRAi/tIlcjbOf5swYQJmzZqF/Px8aGlpsY7DVG5ONqY6fH6h+a0n\nL6New8YiSkQIEYavea0v8P8LuU8e4+alczC1skbJ2ze4GHcYiYf2YYH/X6IJSpiQlL8F3Bcuwahp\n+6l9e9ClzwDcuXENt69dwpDJ04URT+yoqqoiNjYWI0eORO/evRETEwMHBwfWsQghYk7ii72RI0di\n9uzZiIiIwLRpXzm0SUrVa9gY0Xeeso5BCBGyr32tj1/ggz+X/Q95T59ATk4eTc3MMW/TDrSz7SGC\nlIQVcf9bsN13IR7dTcfTrAfIf5GHSUtWftP2jJTrCFj6C0ytOmLm2s2ijM6ckpISIiMjMXr0aAwa\nNAj79u1D3759WccihIgxiS/2NDU14erqim3btsl8sUcIIf/Wd6Q7+o50Zx2DkA9MXroKk5eu+u7t\nnstWw3PZamFEkwgKCgrYtWsXJk6cCGdnZ+zZswfOzs6sYxFCxJRET9BSZcqUKUhNTcWVK1dYRyGE\nEEIIESo5OTkEBQVhwoQJcHV1xcGDB1lHIoSIKako9rp06QJLS0ts27aNdRRCCCGEEKHj8/nw9/fH\npEmTMHz4cMTGxrKORAgRQ1JR7AHvl2HYtWsX8vPzWUchhBBCCBE6Ho+HzZs3w8PDA8OGDcPhw4dZ\nRyKEiBmpKfbGjBkDPp+PiIgI1lEIIYQQQkSCx+PB398f48ePh4uLC44cOcI6EiFEjEhNsffviVoI\nIYQQQmQFj8fD1q1b4erqimHDhuHUqVOsIxFCxITUFHvA/5+o5fLly6yjEEIIIYSIDJ/PR3BwMIYO\nHQonJyecPn2adSRCiBiQqmKvc+fOaNeuHfXuEUIIIUTmyMnJISQkBM7Ozhg4cCASEhJYRyKEMCZV\nxR7wfqKW3bt34/Xr16yjEEIIIYSIlJycHEJDQ+Hk5ARHR0ckJiayjkQIYUjqir3Ro0fTRC2EEEII\nkVlVBV+vXr3g5ORE6xATIsOkrtjT1NTEiBEjaCgnIYQQQmSWgoICIiMjYWdnhz59+uDq1ausIxFC\nGJC6Yg8APD09cfPmTVy6dIl1FEIIIYQQJhQVFbF3717Y2NigT58+SEpKYh2JECJiUlnsWVtb00Qt\nhBBCCJF5ioqKiIqKQocOHdCvXz/cvn2bdSRCiAhJZbEHAJMnT8aePXtoohZCCCGEyDQVFRXExMSg\nVatW6NOnD7KyslhHIoSIiNQWe2PGjIG8vDzCw8NZRyGEEEIIYUpVVRWHDh2Cvr4+evXqhb///pt1\nJEKICEhtsaeurg5XV1f4+/uzjkIIIYQQwpyGhgaOHj0KBQUF9O3bF69evWIdiRAiZPKsAwiTp6cn\nAgMDcfHiRXTp0uWrf+9JZgYuHD0oxGTkexW9ecM6ApEixUVv6bXO0Mvnz1hH+Cw6PqSbuB9/wqKn\np4f4+HjY2NjA0dERJ06cgJqaGutYhBAhkepir2PHjrCyssLmzZu/utjj8Xi4FHcYl+IOCzkdIYS1\nl8+fYd0sL9YxiJii44NIq0aNGiE+Ph62trYYPHgwDh06BCUlJdaxCCFCwOM4jmMdQphCQ0Ph4eGB\nzMxMNGnShHUckVq4cCGOHj2KGzdusI4i89LT02FmZobU1FRYWFiwjkMknK6uLnx9fTFt2jTWUYgU\n6tevHxo2bIjAwEDWUYiQpaSkoHv37ujZsyciIyMhJyfHOhIhpHalS+01e1VGjhwJfX19bNmyhXUU\nkSstLaVP6giRMgKBAK9fv0a9evVYRyFSSk9PD3l5eaxjEBFo27Yt9u/fjyNHjmDGjBms4xBChEDq\niz0FBQVMnToVAQEBKCwsZB1HpKjYI0T6vHjxApWVldDT02MdhUgpKvZkS/fu3bFnzx7s2LEDixYt\nYh2HEFLLpL7YA4CpU6dCIBDgr7/+Yh1FpKjYI0T6VL0Jp2KPCIuenh5yc3NZxyAiNGjQIAQHB+O3\n337D2rVrWcchhNQimSj2tLW1MW7cOPj5+aG8vJx1HJGhYo8Q6VNV7NEwTiIs9erVo549GTRmzBhs\n3LgR8+fPx44dO1jHIYTUEpko9gDgl19+wZMnT2Sqd4+KPUKkT25uLuTk5KCtrc06CpFSenp6ePPm\nDUpKSlhHISI2ffp0LF68GF5eXoiKimIdhxBSC2Sm2DM0NMTEiROxYsUKlJaWso4jElTsESJ98vLy\nULduXZo1jwhN1RDhFy9eME5CWPD19YWXlxfGjh2L043NMnUAACAASURBVKdPs45DCPlBMlPsAcCS\nJUvw4sUL/Pnnn6yjiMS7d++o2CNEyuTl5dH1ekSoqoYI03V7smvjxo1wdnbG0KFDkZaWxjoOIeQH\nyFSx16BBA3h5eWHVqlUoLi5mHUfoSktLoayszDoGIaQW5ebm0vV6RKiqPkyg6/ZkF5/PR2hoKNq3\nb49+/fohOzubdSRCyHeSqWIPABYsWIC3b99i8+bNrKMIHQ3jJET6UM8eETYNDQ0oKytTsSfjFBUV\nER0djbp166J///7Iz89nHYkQ8h1krtjT09PD7NmzsWrVKjx//px1HKGiYo8Q6UPFHhEFXV1dGsZJ\noKmpiSNHjqCwsBBDhgyRmTkPCJEmMlfsAe9797S0tLBw4ULWUYSKij1CpE9ubi4Ve0ToaPkFUqVh\nw4Y4cuQIkpOTMX78eHAcxzoSIeQbyGSxp6KigtWrV+Ovv/7ClStXWMcRGir2CJE+eXl5dM0eETo9\nPT0q9kg1c3Nz7Nu3D/v27cPixYtZxyGEfAOZLPYAYMSIEbCzs4O3t7fUfkpFxR4h0kUgEOD169fU\ns0eEjoo98l89evRAcHAw/u///k8m5j0gRFrIbLEHABs2bMDVq1cRGhrKOopQULFHiHR58eIFKisr\nqdgjQlevXj26Zo98ZNSoUVi2bBlmzpyJAwcOsI5DCPkKMl3stW3bFl5eXpg7d65ULh5LxR4h0qWq\np4WGcRJho549UpMlS5bAy8sLo0aNwsWLF1nHIYR8gUwXewCwatUqKCsrY+bMmayj1DpaVJ0Q6VLV\n00I9e0TYqNgjn+Pn54c+ffpg0KBBuHfvHus4hJDPkPliT0NDA9u2bUN4eDgOHjzIOk6tKisro2KP\nECmSl5cHOTk56OjosI5CpFy9evXw5s0blJSUsI5CxJCcnBwiIiLQokULDBgwgIb8EiLGZL7YA4D+\n/ftj1KhRmDp1qtQsGlpWVgaO46CsrMw6CiGkluTm5qJu3brg8+nUTYSrqveYevdITVRVVXHgwAFw\nHIfBgwfj3bt3rCMRQj6B3jH8w8/PD2VlZVKz9l7VwqfUs0eI9KBlF4ioULFHvkb9+vVx5MgR3Llz\nBx4eHlI7uzkhkoyKvX/o6upi48aNCAgIwPHjx1nH+WFU7BEiffLy8uh6PSISVR8qULFHvsTExAQH\nDhxAdHQ0fH19WcchhPwHFXv/4ubmhpEjR8Ld3V3ix59TsUeI9KFij4iKuro6lJWVJf5vIRENOzs7\nbN26FcuWLUNERATrOISQf6Fi7z+2bt0KFRUVeHp6so7yQ6jYI0T65Obm0jBOIjI0Iyf5Fh4eHvD2\n9sbEiRNx6dIl1nEIIf+gYu8/NDU1ERoaitjYWAQGBrKO892o2CNE+lDPHhElKvbIt1q3bh169+6N\nIUOG4PHjx6zjEEJAxd4n2draYu7cuZg5cyYyMjJYx/kuVbNiUbFHiPSgYo+IEhV75Fvx+XyEh4ej\nXr16GDBgAAoKClhHIkTmUbFXA19fX5iammL48OESuc4Q9ewRIl0EAgFev35NxR4RmXr16tE1e+Sb\nqaur4+DBg3jx4gXc3NwgEAhYRyJEplGxVwNFRUVER0fjyZMn+Omnn1jH+WZU7BEiXfLy8lBZWUnX\n7BGRoZ498r0MDQ3x/9i787Coyv5/4O9hZoBhRxZZFFQW2VFwQ1RcgAxzyQUVEcvc0kefcsnU1LQs\ns8yn0tTMSlMzNXcLA3NjcQNZBBIElX3fZGdm7t8ffplfpKAozBlmPq/rmquYM5zznvEcznzOfZ/7\nPnv2LC5fvoyVK1dyHYcQlUbFXiusrKzw008/4YcffsCPP/7IdZw2aSr2aFJ1QpRD05duatkj8mJi\nYkIte+SF9evXDz/99BO+/PJL7Nq1i+s4hKgsKvae4bXXXsOKFSuwcOFC3L59m+s4z41a9ghRLk3F\nHrXsEXkxNTWllj3yUqZMmYI1a9ZgyZIluHDhAtdxCFFJVOw9h02bNqF///6YNm1ap7nZuKnYU1dX\n5zgJIaQ9FBYWgs/nw9DQkOsoREWYmJigqqoKNTU1XEchndjGjRsxefJkBAYGIjU1les4hKgcKvae\ng0AgwK+//oqqqioEBQV1ipuN6+vrIRQKoaZG/8SEKIOioiIYGxvTMU3kpqnLcHFxMcdJSGfG4/Gw\nd+9e2NjYYOzYsSgrK+M6EiEqhb41PCdzc3OcOXMGFy9exJo1a7iO80z19fXUhZMQJULTLhB5a+oy\nTF05ycsSiUQ4deoUqquraYROQuRMwHWAzsTDwwO7d+/GrFmz4ObmhqCgIK4jAQDy8vIQEREBALIu\nXikpKeDz+YiPj4euri6AxxPGC4VC6OnpcZaVEPJs4eHhCA0NhZmZGYyNjWFiYoKkpCTo6uqiuroa\n2traXEckKoDP5wMALl68iMzMTBQWFqKwsBDFxcVYs2YN3T9K2qTpovmQIUOwevVqfPbZZ1xHIkQl\n8BhjjOsQnc2yZcvw7bff4vLlyxgwYADXcVBQUAALCwtIpdJnvjYoKAgHDx6UQyryTykpKXByckJC\nQgJcXV25jkMU3NWrVzFs2DAIhUJIpdInroKrq6vD0NAQ5ubmWLZsGYKDgzlKSpTJggULcPHiRRQX\nF6OiouKJ/U4gEIAxBm1tbZSVlVGXYvJCDhw4gJCQEBw8eBDTp0/nOg4hyi6F/lK/gC1btsDHxwcT\nJ05Ednb2U18jz+Gqu3btigEDBoDH4z3ztbNnz5ZDIkLIyxg4cCA0NTXR2Nj41O5ODQ0NKCgoQGJi\nIoYOHcpBQqKM+vTpg9TUVJSWlj51vxOLxWCMYcSIEVTokRcWHByMJUuWYPbs2bh16xbXcQhRevTX\n+gXw+XwcOXIERkZGGD16NMrLy5stDwsLg7OzM0pKSuSWKTAwUNblpiUWFhYYMWKEnBIRQl6Uuro6\nvL29W/1CLRQKMWnSJFhbW8sxGVFmISEh0NfXb/U1ampq8Pf3l1Mioqy2bt0KHx8fTJo0ieZyJKSD\nUbH3gvT09PD777+joqICEydORENDAwDg5MmTGDNmDIqLi+U6iejEiRNbveFZKBRi3rx5dDWWkE7C\n39+/1Qs4jY2NWLp0qRwTEWWnpaWFxYsXQygUtvgasViMkSNHyjEVUUZ8Ph+HDh2SXbRq+g5FCGl/\n9M3/JVhaWuLUqVO4efMm3n77bfzyyy+YPHmyrOjaunUr6urq5JLF2tq61XvBxGIxZs6cKZcshJCX\nN3LkSDQ2Nj51GZ/Px6BBgzBw4EA5pyLKbvHixa0uNzY2hoODg5zSEGXWpUsXHD9+HLdv38by5cu5\njkOI0qJi7yV5eHjgl19+waFDhzBjxgxIpVLZQCkVFRU4fPiw3LIEBgY+9Yosn8+Hj48PevXqJbcs\nhJCX07dvX9lIuv8mlUrx3nvvyTkRUQWmpqYICgp66rlEIBBg9OjRHKQiysrNzQ379+/H9u3b8f33\n33MdhxClRMVeO7h//z7q6urAGMM/BzdljGHz5s2Q14Cnr7/++lNbAhhjmDt3rlwyEELaB5/Px/Dh\nw5/aldPS0hLjxo3jIBVRBcuXL4dYLH7iecYYRo0axUEioswmTpyIlStX4j//+Q+uX7/OdRxClA4V\ney/ps88+w5IlS566jDGGu3fvIiwsTC5ZnJycntp6JxKJMGHCBLlkIIS0Hz8/vydG2eXz+Vi5cuUz\nB2Qi5EW5uLhg+PDhEAiaT8UrkUhokC/SITZt2gRfX19MmDABOTk5XMchRKlQsfeCGGN49913sWrV\nqlZfJxAI5Dpx6NSpU5t1v1FXV8fMmTOhpaUltwyEkPYxcuTIJ1pYtLS08MYbb3ATiKiMp7XuWVlZ\n0eivpEOoqanh0KFDMDQ0xJQpU1BfX891JEKUBhV7LygpKQknTpwAj8drdX47sViMixcvIjExUS65\n/t2Vs6GhgebWI6STcnJygrGxsexnoVCIRYsWQUdHh8NURBW8+uqrsLOzk43gLBQK8eqrr3Kciigz\nPT09nDhxAsnJyZg/fz7XcQhRGlTsvSAXFxekpaXhxx9/hKWlZatTGggEAmzdulUuufr16wczMzPZ\nz3Z2dujfv79ctk0IaV88Hg9+fn6y7nRSqRQLFy7kOBVRBTweDytWrJD9LJFIaMoF0uF69+6Nffv2\nYf/+/dizZw/XcQhRClTsvQShUIiQkBCkp6dj586dMDExeeIeB+DxfFiHDh1CXl5eh2fi8XiyUTkF\nAgEWLFjQ4dskhHScUaNGgTEGoVCIwMBAdO/enetIREUEBwfLJllnjGH48OHcBiIqYfz48Vi9ejUW\nL16MW7ducR2HkE6Pir12oK6ujnnz5iErKws7duxAly5dnlr07dixQy55mrpyMsYQFBQkl20SQjqG\nr68vJBIJGhsb8e6773Idh6gQkUgkG4Csd+/eMDU15TgRURUbN27E8OHDMWnSJBQXF3Mdh5BO7cmK\nhLwwDQ0NzJs3DzNmzMD27duxadMm1NXVobGxEY2Njfjmm2+watUqaGtrv/A2qqurUVlZicrKSlRV\nVaGmpqbZjcyVlZVobGyEtrY2HB0dER8fjzt37gB43OpnYGAAdXV16OnpQU9PD4aGhi/9vgkhbdfQ\n0ICKigqUl5ejsrKy2bHMGEN5ebnstUZGRjAzM0NFRQXCw8Ohq6sLDQ0N6Ovrw8DAgI5j8kLq6upQ\nU1OD8vJy2f5XX1+Pmpoa2WucnZ0hEAhgY2ODo0ePyp7X1taGurp6s/UZGBiAx+NBV1cXIpEIOjo6\n0NPTo5FjSZs1Ddji6emJ6dOnIzQ0lPYjQl4Qj8lrEjgVVFZWhq1bt+LLL79EfX09pFIpduzY8cQ9\nN1VVVbh37x6ysrKQl5eHvLw8FBQUIDc3F4WFhSgoKJB9IXza3EcvS19fH3p6ejAwMEC3bt1gamoK\nc3NzmJmZwczMDN26dYONjU2zewFJ26SkpMDJyQkJCQlwdXXlOg7pQI8ePUJaWhoyMzORlZWF/Px8\n5OTkIDc3F7m5uSgtLUVFRUWzL9TtoanwMzExgbm5Obp16yY7fs3NzWFra4sePXo8dbJs0rk1NjYi\nLy8PWVlZKCoqQklJCYqKilBcXIySkpJmj6aLhOXl5XKbA1ZDQwNaWlowMDCAjo4OjIyMYGRkBBMT\nE9n/GxkZwdjYGObm5rC0tKRWRAIAuHHjBoYNG4ZVq1Zh/fr1XMchpDNKoWJPDoqKivDpp59i+/bt\nMDIywoIFC5CRkYF79+4hPT0dBQUFstfq6enJTnRN/+3atSsMDQ1lrXFNj6ar+/+cVkFHRwdCoRA3\nb95E3759UVVVJTuhi8ViPHr0CPX19bLWwfLyclRUVKCyshKlpaXIyclBYWEhcnJyUFBQgMLCQkil\nUgCPr+Ta2NjA1tYWNjY2sLe3h5ubG5ydnV+qtVIVULGnfIqKinD79m3Ex8cjNTUVaWlpSE1NbXZv\nbteuXdG1a1d0794dZmZmsLS0RJcuXWBgYAB9fX1Zgaavrw91dfVmx5G+vr5s4KeIiAg4OjrKfi4v\nL0djY6OsZbDpUVFRgYKCAuTn5yM7Oxv5+fnIyspCdXU1gMf3Gffo0QN2dnbo3bs3HBwc0KdPH7i6\nukIkEsnx0yNtUV1djXv37uHevXvIyMhAdnY2MjMzkZubi6ysLBQUFMj+TgOArq4uTExMYGxs3KyQ\nMjIygo6OjqzwEolEEIlEMDQ0hKamJkQiEfh8PvT09Jpt//79+7C1tW32fGVlJSQSiexnqVSKiooK\n2bLa2lpUV1ejoqICtbW1qKmpQUVFBR49eoSSkhIUFxfLCtOmR11dnWx9Ghoa6NatGywsLGBtbQ0L\nCwtYWVnBzs4Otra2sLKyeurtEkT5fPvtt1i8eDHOnj1LI8IS0nZU7HUEsViMuLg4REdHIyEhAfHx\n8UhKSkJNTQ14PB7Mzc3h5ubWrHBquuquaF+4xGIxcnNzkZ6e/sTj7t27qK6uhpqaGmxsbODu7g5X\nV1cMGDAAXl5eshv7CRV7nV1lZSUiIiIQHR2NuLg43L59Wzbxr6WlJRwcHGBnZwd7e3vZw9ra+olu\nblyprKzEvXv3ZAVp0+Pvv/9GZWUlBAKBrPDz8PDAkCFD0LdvX/oyLWcPHz5EQkICkpKSkJaWJivw\ncnNzATzu2tatWzd0794d3bt3lxVAlpaWsLS0hJWVFUxMTBRmv2ur6upq5OTkICcnB9nZ2cjKykJu\nbi4yMzORk5ODhw8foqSkBMDje+V79OgBW1tb2cULV1dXuLq60rlHCb3xxhs4ffo0YmJi0LNnT67j\nENKZULHXHqqqqnDlyhVER0cjMjISN27cQHV1NQwNDdG3b1/ZCcjd3R3Ozs6ykTI7O6lUioyMDMTH\nxyMxMRGJiYmIj49Heno61NTU4OzsDG9vbwwePBg+Pj6wsrLiOjJnqNjrXCoqKnDp0iVcunQJV69e\nRVxcHCQSCezt7eHh4YG+ffvKHv+cB6+zYYzh3r17iIuLQ2xsLG7fvo2YmBgUFxdDV1cX3t7eGDZs\nGHx8fDBw4EC6Z6adNDY2IiEhAbdv30ZCQoLsomDTfZrW1tayFqymYsbOzg69evWChoYGx+m5VVZW\nJiuC09LSZEXx3bt3UVZWBgDo2bMn3NzcZA9PT08qEDq52tpaeHt7gzGGqKgohbswTogCo2LvRWVk\nZODMmTM4e/Ysrl69ivr6epibm2PIkCHw9vaWXRlvbf49ZVVRUYGbN28iIiICkZGRiIyMRG1tLXr1\n6gVfX1/4+vpi9OjR0NXV5Tqq3FCxp/ju37+PsLAwnDlzBn/++ScaGhpk+6y3tzdGjBihMtMeZGRk\nyI7f8+fP4+HDh+jSpQtGjRqF1157DePGjYOBgQHXMTuN/Px83Lx5EzExMYiMjERUVBRqamqgrq4O\nW1tbeHp6wtPTE87OznB3d4eJiQnXkTul3NxcJCcnIykpCTExMUhOTkZiYiIaGhrQtWtX9O/fH56e\nnhgyZAgGDx7c7BYIovju3buHfv36Ydq0adi1axfXcQjpLKjYe15SqRRRUVE4fPgwjh8/jry8PJia\nmsLf3x+jR4+Gn58f3VDegvr6ekREROD8+fM4f/48EhISIBKJ4Ovri6lTp2LcuHFKX/hRsaeYMjMz\nceDAARw6dAhJSUkwMDDA6NGj8dprr2H06NEwMjLiOqJC+Pvvv3HmzBmcO3cOkZGR4PF4GDVqFIKD\ng/H666/Tl+Z/KSkpwYULF/Dnn3/ir7/+wv3796GmpgYnJyd4eXnBy8sLgwYNQu/evVXygqA81dbW\nIiYmBteuXUNUVBSuXbuGvLw8CAQCeHh4wNfXF35+fhg8eHCn7f6qSk6fPo0JEyZg7969ePPNN7mO\nQ0hnQMXes8TGxuLgwYM4cuQIsrOz4eLigsDAQIwZMwZ9+/YFj8fjOmKnk5ubi9DQUPz2228ICwuD\nQCBAQEAApk+fjrFjxyrlCZeKPcVRU1ODI0eOYN++fbhy5QqMjIwwbdo0TJw4EUOGDFGKLtYdqbS0\nFKGhoTh8+DBCQ0OhqamJiRMn4o033lDZSbclEgkiIyMRGhqKsLAwxMbGQk1NDQMGDICfnx+8vb0x\ncODAJwY+Idx4+PAhoqKicOXKFYSFhSE9PR3a2toYPnw4/Pz8MGbMGNja2nIdk7Rg5cqV+PrrrxEZ\nGQkPDw+u4xCi6KjYe5r6+nqcPn0a3333HcLDw2FtbY3x48cjJCQEnp6eXMdTKmVlZThz5gyOHj2K\n8+fPw9DQEG+++Sbmz5+vVPdYULHHvfz8fOzatQvbt29HVVUV/Pz8EBISgvHjxyvlBQZ5KCsrw9Gj\nR7F//35ERUXB3t4eb7/9NubOnav0rX0SiQTR0dE4evQojh49iry8vGZd1X19fWn+w04iIyMD4eHh\nCA8Px4ULF1BaWgonJydMmTIFgYGBcHJy4joi+QeJRIJXX30VaWlpiImJQZcuXbiORIgiSwEjMnl5\neWzZsmXM0NCQqaurs+nTp7PLly9zHUtlZGVlsbVr1zJzc3PG5/PZ2LFjWUREBNex2kVycjIDwBIS\nEriOonKSk5PZ9OnTmVAoZObm5uzjjz9mRUVFXMdSOrGxsSwkJISpq6szExMTtmHDBlZZWcl1rHYX\nGRnJ3nrrLWZkZMQAsD59+rCPPvqIJScncx2NtIPGxkYWFhbGFixYwLp27coAMCcnJ7Zx40aWnZ3N\ndTzyf/Lz85mFhQWbMGECk0qlXMchRJElU7HHHv/RePfdd5lIJGIWFhbsk08+YQUFBVzHUlkNDQ3s\n6NGjbMiQIQwA8/f3Z5GRkVzHeilU7MlfVlYWmzNnDhMIBMzV1ZX9/PPPrL6+nutYSi8vL4998MEH\nTF9fn5mYmLCvvvqq03/uZWVl7Ouvv2YuLi4MAHN3d2ebN29m9+7d4zoa6UBisZhdunSJLV68mJmY\nmDA+n8/GjRvHzpw5w8RiMdfxVN6VK1eYQCBg27Zt4zoKIYpMtYu96upqtnr1aqalpcXMzc3Z//73\nP1ZTU8N1LPIPFy5ckBV9r776Krt79y7XkV4IFXvyU19fz9avX89EIhGztrZm+/btYxKJhOtYKqe4\nuJgtW7aMaWpqsl69erHff/+d60htdu/ePTZnzhwmEomYjo4Oe+utt9j169e5jkU4UFdXxw4fPsxG\njhzJeDwe6969O/viiy9YVVUV19FU2scff8yEQiGLioriOgohikp1i72zZ8+ynj17Mn19fbZ161Yq\n8hRcWFgYc3d3ZxoaGmz9+vWstraW60htQsWefNy4cYO5uLgwbW1t9sUXX7C6ujquI6m8hw8fsmnT\npjEAbNasWay0tJTrSM+UkpLCZs6cyQQCAbOzs2M7d+5kFRUVXMciCiI1NZUtW7aM6ejoMBMTE/bJ\nJ58oZZflzkAikbBXXnmFWVlZsZKSEq7jEKKIVK/Yq6ioYFOnTmUA2LRp01hubi7XkchzamxsZFu3\nbmU6OjrMzs6O3bp1i+tIz42KvY4llUrZhx9+yAQCARs5ciTLyMjgOhL5l5MnTzJzc3Nmbm7OLl26\nxHWcp8rPz2czZ85kampqzNHRkf3888/UXY+0qKioiK1evZrp6emxLl26sG3btrHGxkauY6mcwsJC\nZmFhwcaOHUv37xHypGSVmuAnOTkZAwYMwOXLlxEaGopffvkF5ubmXMciz0kgEGDp0qVISUmBtbU1\nhgwZgr1793Idi3CspqYGU6dOxaZNm7Bt2zaEh4cr1UiuymL8+PFISkqCl5cX/P398f3333MdSUYq\nlWLXrl1wcHDAlStXcOjQIdy5cwfBwcHg8/lcxyMKytjYGJs2bcKDBw+wYMECvP/++xgwYACuX7/O\ndTSVYmJigkOHDuH333/H9u3buY5DiMJRmWLvzJkzGDhwIIyNjRETE4NXXnmF60jkBXXr1g2hoaFY\nunQp5s6diwULFkAqlXIdi3CgtLQUPj4++Ouvv/Dnn3/iP//5D819qcAMDQ1x7NgxvP/++5g3bx5W\nrlzJdSQ8ePAAQ4YMwZIlSzBv3jwkJSVh6tSpNNk5eW6GhobYtGkT4uPjYWhoiMGDB+O///0vGhoa\nuI6mMnx8fLB+/XosW7YM165d4zoOIQpFJebZO3fuHCZOnIiQkBB8++23EAqFXEci7eTUqVOYNm0a\nZsyYgT179ijsF32aZ6/9VVdXw9fXF/n5+QgPD4eNjQ3XkUgbHDp0CCEhIVi3bh3WrVvHSYaLFy8i\nMDAQ3bp1w/79++nYJC+NMYYDBw5g0aJFcHd3x7Fjx9C1a1euY6kEqVSK0aNHIyMjAzExMdDX1+c6\nEiGKIEXpL12GhYVh8uTJCAkJwXfffUeFnpIZP348fvvtN9nJlagGsViMyZMnIyMjA+fPn6dCrxMK\nCgrCzp078eGHH2L37t1y3/6OHTvg7++PUaNGITIykgo90i54PB5mzpyJ6Oho5OXloX///oiLi+M6\nlkpQU1PDgQMHUFtbizlz5nAdhxCFodTFXkFBAWbMmIGJEydi9+7dCtvqo8jWr18PHR0d9OjRg+so\nLQoICMCvv/6K3bt34+DBg1zHIXKwbds2XLp0CX/88Qfs7e25jtMhAgMDwefzsXz5cq6jdJi5c+di\n3bp1+O9//4u///5bbtv9+uuvsXjxYmzYsAG//PILtLS05LbtzmLVqlXQ0NDAhAkTOmwbK1euhI+P\nT4etn0vOzs64ceMG7Ozs4Ovrizt37nAdSSWYmpri0KFDOHHiBHbs2MF1HEIUglIXewsXLoSOjg52\n7dol9/svwsPDm90o/O+fO4sNGzZg8+bNXMd4pvHjx2Px4sVYtGgRsrKyuI5DOtCDBw+wYcMGrFu3\nDh4eHnLdtjyP6yNHjmDo0KEdsm5FsnbtWri6umL27Nlyuff2/PnzePfdd7F582asXr2as4uAin6O\n+PTTTzFz5kyuY8go2ufzPLp06YIzZ87A2dkZ48aNQ2lpKdeRVIKPjw8++OADLFu2DLGxsVzHIYRz\nSlvsnT59GidOnMC+ffugq6sr9+3v2bOn1Z9J+/v0009hZmaG9957j+sopAOtWrUKvXr14qTFi47r\n9sfn8/H999/jxo0bOHHiRIduq7y8HLNmzcL06dM5/ztB+xLw2Wef4fLly8/12s76+WhpaeHYsWMQ\ni8VYvHgx13FUxrp16zBs2DAEBgaioqKC6ziEcIvjuR86jLe3Nxs/fnyHrPvNN99kPB6Pff7554wx\nxrZt28a0tLSYp6cnY4yx2bNnMx6PxwAwACwgIKDZz3l5eSw9PZ35+/szPT091qNHD/b9998zxhhb\nvXo1U1NTY6tWrWKbNm1iPXr0YHFxcS+V5+OPP2Z8Pp9NmzaNzZgxgxkaGjJLS0sWFhYmW0dBQQEL\nCgpilpaWTF1dnVlaWrLDhw8zxhj75ptvmLW1tey1LWVnjLEvvviCWVhYMKFQyKysrNiRI0de+H29\niMOHDzM+n88ePHjQ7ut+GTTPXvsoKSlhmpqaVLB2dQAAIABJREFU7Mcff2z3dSvacc0YYz4+Puz1\n119nQUFBrEuXLszc3JydO3dOtryl4239+vWMx+OxESNGsF69ejEtLS3m4+PD0tLSnmt5YGAgA8C0\ntLTYF198wRhj7K233mJCoZD169evnT7x5gICAlhAQECHrLvJihUrmKmpKSsrK+vQ7SjivtTSvsIY\nY5s2bWKmpqZMU1OT2dnZsfz8fMbY43/zpvNodXU1s7KyYlpaWmzPnj3P3N6z9rFjx44xfX19Zmlp\nKfudls5D//68Xn311VY/35Y+o9bOXR3t1KlTjMfjsevXr8ttm6ouLy+Pde3alU2dOpXrKIRwSTkn\nVc/MzGQ8Ho+dPXu2w7bRu3dv2YmGMcY++ugj2YmGMcasra3ZN99889SfxWIxs7GxYQsXLmQVFRXs\n/PnzzU4CPj4+bNSoUezy5cvsiy++YLdv337pPD4+Pmz+/PmsurqaPXr0iI0ePZoNGTJEttzLy4sN\nGDCApaWlsZqaGrZv3z62bds2xljzYu9Z2d955x0WHx/Pamtr2bJly5oViS/yvtqqoaGBGRkZsS1b\ntrT7ul8GFXvtY8eOHUxXV5c9evSoQ9avaMf1P4/b6upqNnHixGbFVmvHm7u7O1u0aBGrqqpiOTk5\nbNSoUczV1fW5l9vb27OQkJBmeYYOHcpqa2ufmftF/Pbbb4zP57Pc3NwOWX9tbS0zNDRkn376aYes\n/98UbV9qaV+5desW69KlC7t//z6rqqpiK1asYPfv32eMNS/2amtrWVBQEEtOTn7uz+BZ+9jOnTub\nFXutnYf+/Xk9zznvn5/RrVu3Wv1M5aFfv34sODhYbtsjjJ0/f56pqamxH374gesohHBFOSdVj4qK\nAp/Px8iRI7mO8lQ3btxAeno6Vq9eDT09Pfj7+8PW1hbh4eGy1/Tp0wfDhg3DsmXL0KdPn3bZro6O\nDrS0tKCjo4NXX30V2dnZAICYmBhER0djy5YtsLW1hUgkwowZMzB+/Pg2Z9+2bRvc3NygqamJQYMG\noaCgoNnvd8T7+iehUIiRI0ciIiKi3ddNuBcREQEfHx/o6OhwHeUJHXVcNx23WlpaeOWVV2THLfDs\n483MzAza2tqwsLDARx99hMTERNy+ffu5ls+ZMwe//fYbqqqqAADx8fFwcXGBpqbmC39GrRk9ejQA\nIDo6ukPWHxUVhbKyMgQFBXXI+ttTR+xLLe0rRUVFqK2thVgshra2NrZs2fLEgFwNDQ1YsmQJ1q9f\nD0dHxza9l2ftg03ach56Xv/8jBoaGp75mXa0GTNm4I8//qB5YeXI398fS5cuxeLFi3H37l2u4xDC\nCaUs9jIyMtCjRw+IRCKuozzVgwcPADyeHJzH44HH4yEtLQ1lZWVyy6Cmpgb2f1MspqSkAECzocf5\nfD569uz5xO89K/vnn38Oe3t7aGtrIzAwULYNeXJ0dER6errct0s63oMHD2BnZ8d1jKeSx3H9z+MW\naNvx1rt3bwDA/fv3n2v5G2+8gYaGBhw7dgwAcODAAcyePbtd3sfTaGlpwdLSssV8Lys5ORmmpqaw\nsrLqkPW3p47Yl1raV4YOHQp7e3s4OzsjMDDwiWJbLBZj2rRpuH79+lPPCW3R2j7YlvPQi1CE826/\nfv1QUlKC/Px8uW2TAJ988glcXFwQFBREE90TlaSUxV5NTY3CFnoAoK6uDgCoqKgAY0z2+PzzzznJ\n03Sl/nnmIGwt+6VLl7By5Up88sknyMvLw+HDhzs0d0u0tLRQU1PDybZbQtN+tI/a2lqFPbblfVy3\n9XhrbGwEAAgEgudabmJigvHjx2Pfvn2QSqW4c+cO+vXr147v4EkikajDjt3q6mqFbBF+mvbel1rb\nV7S1tXHjxg389NNPKCkpgbe3N37//XfZ8r/++gvTp09Hbm4uNm7c+FLvq7V9sC3noRehCOfdpsHi\nmlrLiXwIhUIcOHAAaWlpWLNmDddxCJE7pSz2unTpgpKSkg7dBo/He+GuGE1XlpuuZHKdx9bWFgCe\na+LX1rKnpKTAwsICkydPhp6entynu2hSVFQEIyMjTrbdkqZij7rvvBwDA4MOHb5c0Y7r1rT1eGua\n56ulltGnLZ87dy4uX76MH3/8Ef7+/u2UvGUlJSXo0qVLh6zbxMQEhYWFkEgkHbL+f1OkfelZ+4q6\nujqmT5+OCxcuwN/fHydPnpQt8/Pzw5QpU7Bjxw5s3rwZMTExL5yjtX2wLechoO2fr7yPz6fJyckB\n8HguOCJftra2+Oqrr7B161acO3eO6ziEyJVSFnsuLi7Izc1FXl5eh23D3NwcFy5cQFVVFcrLy5/Y\nlkgkws2bN1FfX4+6urpmP7u4uMDBwQFLly7FgwcPIJFIkJubi0ePHnVYntb06dMHXl5eWLRoEW7c\nuIG6ujpUVlY+db66fv36tZjdwsICRUVFSElJQWlpKW7duvXC7+dlxMTEwMXFhZNtt6TpyxUVey/H\nycnpub8MvghFO65b8zzHW3V1NRobG5GZmYm1a9fCx8en2T1Xz1ru5+cHa2trLF++HMHBwR3yPppk\nZ2ejuLgYTk5OHbL+gQMHoqqqCjdv3uyQ9f+bIu1Lre0rx44dw7fffovq6mqUlJSgtrYW3bt3ly1v\nulAVGBiI119/HbNmzWpTV7hn7WNNnnUe+vfn1dZzXmvnLnm5ePEiHBwcYGBgILdtkv/vzTffxPTp\n0zF79mzqSktUi3wHhJGP6upqpq2tzb799tsO20ZoaCjr2rUr09PTY+PHj2fTp09nampqbPHixYwx\nxr788ksmEomYlZUVi4iIeOLne/fuseHDhzNNTU2mr6/PAgMDWX5+PluzZg3j8/lMQ0ODzZ49u13y\nbNq0ifH5fCYSidiuXbvYuXPnmL6+PgPA5syZwxhjrKioiE2bNo3p6+szdXV15u7uzs6ePcs2bNjA\ndHR0GADm5ubGCgsLW8xeV1fHXnvtNSYSiZi7uzvbuHEjA8D8/f1f+H21VWFhIRMIBOyXX37psG28\niHv37jEA7NatW1xH6dTOnTvHeDweu3fvXoesX5GO66bfaTpuw8LCmKGhIQPAgoODWz3eGHs8EqKh\noSFTV1dnIpGITZgwgRUVFcnW/6zlTT766CP2+uuvt8On27otW7awLl26dNhon4wx5uLiwmbNmtVh\n6/8nRdqXWttXIiIimI2NDRMKhczQ0JBNnTqVVVZWsu+++45paGgwoVDIDh06xBhjzMrKigFgdnZ2\nz7Xd1vaxjz/+WHZucXV1Zfn5+S2eh572ebX2+bb0GbX0mcpDdXU1MzExYevXr5fL9sjTlZeXs549\ne7LRo0czqVTKdRxC5EE5p15g7PE8Rw4ODkwsFnMdhcjRunXrmLGxMauuruY6SjMZGRkMALtx4wbX\nUTo1sVjMLC0t2QcffMB1FIXn7u7ONm3a9MLLm2zevJmFhoa2Z7QnSKVS5ujoyBYtWtSh2/n5558Z\nn8+niy5y8rz7mCr44IMPmL6+PisoKOA6isq7fv06EwqF7KuvvuI6CiHyoJxTLwDAqlWr8ODBA84G\nPWkvDx48kI0c1tKjaZQxVff333/j888/x/Lly6GlpcV1nGaoG2f74PP5mD9/Pr7++uundjPuLOR1\nXD9rf2tp+Y8//ojKykqEhobiypUreOWVV146S2v27t2L1NRUzJ8/v0O3M2PGDAwbNgxBQUGoqKjo\n0G3JCxfniLZsk/7mPe6+uXnzZmzatInu11MAAwYMwNq1a/Hee+916G0BhCgMrsvNjvT5558zdXV1\nFh8fz3UU0sEaGxvZwIEDmaenJ2toaOA6zhMyMzMZABYVFcV1lE6vrq6OOTo6soCAAK6jKKyFCxcy\nHo/HRCLRU1vLnrV8/vz5TCgUsuHDh7Ps7OwOzZqXl8cMDQ3ZihUrOnQ7TXJzc5m5uTkbNmyYwvUA\nUCbP2sdURWxsLDMwMGCBgYHUbVCBSCQSNnLkSObo6Eh/B4iyS+YxxsFEaHIilUoxYsQI5OTk4NKl\nS+jWrRvXkUgHkEqlmDNnDn799VfExMTAwcGB60hPyMnJQbdu3RAREQFvb2+u43R6V65cwYgRI/DJ\nJ59g5cqVXMchL6i2thavvPIK8vLyEB8fL7cW+Tt37mDEiBHo2bMnTpw4AUtLS7lsl6iWP/74A0FB\nQejfvz9Onz4tm16CKIbs7Gz06dMHkydPxq5du7iOQ0hHSVHabpzA465zv/32G0QiEUaOHInc3Fyu\nI5F2xhjD4sWLcfDgQRw5ckQhCz2AunG2t2HDhmH79u1YtWoV9u7dy3Uc8gIkEgmCg4ORnJyM06dP\ny7XrtYuLC6Kjo1FTU4O+ffvi8uXLcts2UQ1fffUVxo4di4CAAJw6dYoKPQXUrVs37N69G999912z\n6UYIUTZKXewBgLGxMcLDw8Hn8+Hj4yOb54d0fjU1NQgJCcGePXtw7NgxjBkzhutILaJir/29/fbb\nWLt2LRYsWIDdu3dzHYe0QXV1NQIDA3H+/HmcPXv2qUPxdzRbW1tERUVh0KBBeOWVV/Dhhx+irq5O\n7jmIcnnw4AFee+01LF++HF988QUOHjwIkUjEdSzSgkmTJmH27NmYO3duh07XRQiXlL7YA4CuXbvi\n4sWLsLCwwKBBg3Do0CGuI5GXlJaWhkGDBuGPP/7A2bNnMXbsWK4jtYqKvY6xYcMGrF27Fm+//TaW\nLFkCsVjMdSTyDFlZWRg6dCiuXLmCP/74A4MGDeIsi56eHk6ePIlPPvkEW7duhZubG8LDwznLQzqv\nxsZGbN68Gc7OzsjIyMBff/2Fd955h+tY5Dl89dVXMDQ0xBtvvAElvrOJqDCVKPYAwMzMDBcuXMD8\n+fMRHByMN998E8XFxVzHIm0klUqxa9cu9O/fH5qamoiJiYG/vz/XsZ6Jir2Os27dOhw5cgR79+6F\nn58fMjIyuI5EWnDq1Cn0798fYrEYN27cwNChQ7mOBDU1NSxduhTJyclwcXGBn58fXn/9dcTGxnId\njXQCEokEBw8ehKurKzZu3Ig1a9YgLi5OIfZt8ny0tbWxb98+XLhwATt37uQ6DiHtTmWKPQAQCATY\nunUrTpw4gbCwMDg4OOCHH36gKzmdRFxcHAYPHowlS5ZgwYIFuHr1KqytrbmO9Vyo2OtYkydPRmRk\nJEpLS+Hm5oavv/6aPmsFUlxcjOnTp2PChAkICAhAZGQkevbsyXWsZrp3747jx4/j3LlzyMrKQr9+\n/TB27Fhcv36d62hEAYnFYvz0009wdHTEG2+8gQEDBiApKQmrV6+Guro61/FIG3l5eWHVqlVYtmwZ\nkpKSuI5DSLtSqWKvyfjx45GSkoKZM2di3rx58PLyQmhoKNexSAvu37+PuXPnon///hAIBIiNjcXm\nzZuhoaHBdbTnRsVex+vTpw9u3ryJ5cuXY/ny5fDy8sKlS5e4jqXSGhoa8M0338DJyQmRkZH4448/\n8MMPP0BXV5fraC0KCAjArVu3cPbsWRQXF2PQoEEYPnw4Dh06RPf0EeTm5mLTpk2wtbXFvHnzMGTI\nEKSkpGD//v0KdwGDtM369evh7u6OkJAQNDQ0cB2HkHajksUeAOjq6mLbtm24desWjI2N8eqrr2Lw\n4ME4f/4819HI/3nw4AHmzp2L3r174+LFi9i7dy+uXr0KFxcXrqO1GRV78qGuro4PP/wQt27dgr6+\nPkaMGIGAgACaOFfOpFIpDhw4AAcHB7z33nsICQnBnTt3MHr0aK6jPbeAgABER0cjLCwMXbp0waxZ\ns9CtWzcsXboUKSkpXMcjciSRSPD7779jwoQJsLa2xv/+9z9MmjQJqamp+OGHH2Bra8t1RNIOBAIB\nDhw4gNTUVGzcuJHrOIS0H06n+VMgcXFxbMqUKYzH4zEHBwe2efNmVlZWxnUslXT16lU2ZcoUJhQK\nmbW1Ndu9ezdrbGzkOtZLqaqqYgDY2bNnuY6iUiIiIpi3tzfj8XjM19eXnT59miY27kC1tbVs3759\nzMnJiampqbEpU6awjIwMrmO1i7y8PLZ582bWq1cvBoA5OTmx9evXs+TkZK6jkQ4gFovZ1atX2ZIl\nS5i5uTkDwDw9Pdnu3btpEm4lt2PHDqampsYuXbrEdRRC2oNyT6r+ImJiYrBjxw4cPnwYQqEQM2fO\nxOzZs+Hh4cF1NKVWUFCAX375Bbt27cLdu3cxZMgQLFy4EJMnT4ZQKOQ63kurra2FlpYWTp8+rfAj\nhyobxhjOnDmDbdu24dKlS3B1dcWSJUswdepUhe5O2JlkZGRgz549+O6771BbW4vg4GAsXbpUYee9\nfBlSqRQXL17E0aNHceLECRQWFsLFxQWTJk3C2LFj0bdvX1lLPulcqqurcfnyZZw4cQInT55EcXEx\n3N3dMWnSJEybNg12dnZcRyRywBjDuHHjkJSUhLi4OOjp6XEdiZCXkULFXgtKS0vx008/YdeuXUhL\nS4OdnR2mTp2KqVOndspuhIqopKQEx48fx6+//opLly5BS0sLM2bMwNtvvw03Nzeu47Wr+vp6aGpq\n4uTJkxg/fjzXcVRWXFwc/ve//+Hw4cPg8/mYMGECgoOD4e/vDz6fz3W8TqWsrAxHjhzBzz//jKio\nKJibm2PhwoWYP38+jI2NuY4nFxKJBFevXsWxY8dw4sQJ5ObmwtjYGCNHjoS/vz98fX07zSBSqkgi\nkSA2NhZhYWEICwtDVFQUGhoa4OnpicmTJ2PSpElU4KmowsJCuLm5YcyYMdi7dy/XcQh5GVTsPY8b\nN27g119/xdGjR5GVlQVHR0eMGTMGo0ePxpAhQzrVQCFcS0pKwvnz5xEaGopLly5BIBBgzJgxmDp1\nKgICAqClpcV1xA7R2NgIdXV1HD9+HK+//jrXcVTevwuVrl27Yty4cXjttdfg6+tLkyC3IDc3F2fP\nnsWZM2cQFhZGBfM/MMaQkJCAsLAwhIeH48qVK6itrYW9vT28vb0xePBgDBo0CE5OTtTyx5Gamhrc\nvHkT0dHRiI6ORkREBEpLS2Fubg5fX1/4+fnB19cX5ubmXEclCuDUqVOYMGECjhw5gilTpnAdh5AX\nRcVeWzDGEBkZiRMnTuCPP/5ASkoKtLW1MWLECPj7+2PIkCFwc3NT6S88/5aVlYXIyEj89ddfCA0N\nRVZWFoyMjODn54dx48Zh7Nix0NHR4Tpmh5NIJBAIBDh27BgmTZrEdRzyDxkZGTh06BBOnz6NW7du\nQVNTE6NGjUJAQAB8fHzg6OgIHo/HdUxO1NXV4ebNmwgPD8e5c+cQGxsLkUgkm4tu4sSJ1BW2BXV1\ndbK/fZGRkbh16xaqq6uhr6+PgQMHwsvLC3379oWbmxuN4tgBGhoakJycjISEBFmBFx8fD7FYDAsL\nCwwaNAhDhw6Fr68v9dYhLXrrrbdw+vRpJCYmwszMjOs4hLwIKvZeRmZmJkJDQ3H+/Hn89ddfKC8v\nh46ODgYMGABvb294eXnBw8MDXbt25TqqXNTU1CAxMRHXrl1DdHQ0IiMjkZ2dDYFAgP79+2P06NF4\n5ZVX0K9fP5UriBljUFNToyuECi4vLw/nzp3D2bNnceHCBVRVVcHExARDhw7FsGHD4O3tDTc3N6Wd\nR6u0tBQ3b95EREQELl++jJs3b6Kurg5WVlYICAjA2LFjMXLkSGhqanIdtdMRi8VITExEVFQUrl27\nhmvXriE9PR2MMejp6cHV1RVubm5wd3eHi4sL7O3tYWJiwnVshSeRSJCZmYm7d+8iPj4eCQkJSEhI\nwN27d9HY2AgNDQ306dMHgwYNwqBBgzB48GBYWVlxHZt0Eo8ePUKfPn3g7OyM06dPcx2HkBdBxV57\nkUqlSElJQVRUFCIjIxEdHY3U1FQAgKmpKdzd3eHm5gY3Nze4uLjAxsYG+vr6HKd+MQ0NDXj48CFS\nUlJkJ9b4+Hikp6dDIpHAyMgIXl5e8PLygre3N/r376+03TPbQiAQYP/+/QgKCuI6CnkOYrEYsbGx\nuHr1Ki5fvoyIiAiUlZVBKBTCxcUFffv2Rd++fdGnTx84ODh0qvvUpFIpMjMzkZSUhNu3byM2Nha3\nb9/GgwcPAAB2dnayAnfYsGHU8tRBHj16hMTERCQmJiIuLk72/5WVlQAAAwMD2Nraws7ODra2trC3\nt4eNjQ2srKxgZmamMhfNamtrkZ2djYcPH+LevXtIS0vDvXv3kJqaioyMDNmcaFZWVs2KZldXV9jb\n20MgEHD8DkhnFhkZCR8fH+zZswdvvvkm13EIaSsq9jpSSUkJ4uLiZAVRQkICkpKSUF9fDwAwNjaG\njY2N7NGjRw+YmZnBwsICZmZmMDU15eTejoqKCuTm5qKgoAA5OTnIyclBenq67JGVlQWJRAIej4ce\nPXo0K2Td3d1hY2Ojst3eWqOlpYWdO3di1qxZXEchL0AqleLu3bu4fft2swKprKwMAGBoaAg7OzvY\n2dmhd+/e6NGjBywsLGBhYQFLS0vo6enJBnvqaIwxFBQUIC8vr9kxnJaWhtTUVKSnp8v+DvXq1UtW\nuPbt2xeenp4q0xtBETHGkJmZKSto/lnY3L9/X/bvxufzYWZmhu7du8PS0hLdunVDt27dYGpqCiMj\nIxgbG8PIyAimpqYKOZqgRCJBSUkJiouLUVJSgpKSEhQVFSE3Nxc5OTnIzc1FZmYmcnNzUVJSIvs9\nQ0NDWQH8z4e9vT0MDAw4fEdEmS1btgzff/89EhMTqWWYdDZU7MmbWCxuVjj98/Hw4UPU1NTIXsvn\n89G1a1eYmprCwMAAenp6zR5NJzZ9fX1ZUaihoSFrRROLxXj06JFsfY8ePYJYLEZNTQ0qKytRWVmJ\niooKVFRUoLy8HKWlpcjLy0Ntba3sdwQCAczNzZsVpU0POzs7hfwSoagMDAzw+eefY+7cuVxHIe3o\n4cOHSEtLkz1SU1ORlpaGzMxM1NXVyV6nqakJoVAIZ2dnGBgYQF9fH/r6+jA0NISBgQF4PJ7svwCg\nra0t6y5aXV0ta72or69HTU0N6uvrZcdu03FcVlaGgoICFBQUoLGxUbZtAwMD2TH7zy/HvXv3pi/I\nnYhEIkFOTg6ysrKQnZ0tK4iaCvqsrCwUFRU12+8AQCgUwsjICEZGRtDW1oaenh50dHQgEomgq6sL\nXV1daGpqyu6/1NLSajbwGJ/Pb/a3XiqVoqKiotk2ms4vjDGUl5ejpqYGtbW1qKioQHV1Nerq6lBR\nUYFHjx6huLgYpaWlT7w/PT092QWSbt26oXv37rCwsED37t1lP3emFnSiPOrr62UXwsLDw+mCNulM\nqNhTNI8ePUJOTg4KCwtlrWsFBQWoqKiQFWf/LNSaTqxNmr4EApB9eQQeXy0Wi8UwMjKCjo4O9PT0\noK+vLysam750WlhYwNTUVHaF2NTUlP6otRMTExNs2LABCxcu5DoKkZPi4mLk5+dj165d+PbbbzFk\nyBD069dPVqA1/besrOyJL9CVlZWQSCQAAJFIJLtPjs/nQ0tLC1paWtDX15cdvwYGBjA0NISJiYms\nxcfc3ByWlpbUjVrFVFdXy1rK/tlyVlxcLLvYV1VVhdraWjx69AiVlZWora1FdXU1gP9fuDVpaGiQ\nLWvyzwsTQPN91NDQECKRCCKRCAYGBrL9VU9PD7q6ujA2NoaJiYmsAG16KOu9sEQ5xMbGYtCgQdi2\nbRsWLVrEdRxCnlcKdWRXMLq6unBwcGj3yYirqqqgr6+P/fv309D/HFFXV5e1zhDVYGxsjO3bt2PH\njh0AgDlz5iAkJOSl1jlv3jykpqbi0qVL7ZCQKCNtbW1oa2u3e3czV1dXTJw4ERs2bGjX9RLSGXh4\neGDlypV477334O/vT3Mwkk6DJvtRETo6OrC3t0dsbCzXUVSWUChs1rWOKDeJRIIFCxZg48aNsuec\nnJxeer1ubm6Ij48HdcoghBD5Wr9+PRwdHTFr1ixZzwtCFB0VeyrE09MTMTExXMdQWdSypzoaGhow\nbdo07NmzR1aU8Xg89O7d+6XX7e7ujvLycmRlZb30ugghhDy/plG1b9++jS+//JLrOIQ8Fyr2VIiH\nhwcVexyilj3VUFVVhYCAAJw8eRJSqVT2fNeuXdtlAnI3NzfweDwkJCS89LoIIYS0jZOTE9auXYu1\na9ciMTGR6ziEPBMVeyrEw8MDhYWFyM7O5jqKSqKWPeVXUFAALy8vXLlypdkAFwDg7OzcLtvQ19eH\nlZUV4uPj22V9hBBC2ub9999H//79MWvWLLqISxQeFXsqxMPDA2pqanTfHkfU1dXppKDE7t+/j0GD\nBuHu3btP/Durq6vD1dW13bbl7u5OV5QJIYQjampq+P777/H333/j008/5ToOIa2iYk+F6OnpwcbG\nhoo9jgiFQmrZU1KJiYkYOHAgcnJynlrQS6VSODo6ttv2mgZpIYQQwo3evXvjk08+waZNm+jvMVFo\nVOypGLpvjzvUsqecLl26BC8vL5SVlbX47ysWi9u92EtLS0NNTU27rZMQQkjbLFmyBAMHDqTunESh\nUbGnYjw8PHDr1i2uY6gkatlTPmfOnIG/vz9qamqeuEfv39pz7kx3d3dIJBIkJye32zoJIYS0TVN3\nztTUVGzZsoXrOIQ8FRV7KsbT0xP5+fnIy8vjOorKoZY95TNixAisXr0ampqaEAqFLb7OwMAAJiYm\n7bZdW1tbaGtrU9chQgjhmL29PTZu3IiNGzfizp07XMch5AlU7KkYDw8P8Hg8um+PA9Syp3x0dHTw\n4YcfIjMzE++++y4EAsFTi7727MIJPL6a7OzsTIO0EEKIAli6dCn69++PkJAQuqhLFA4VeyrG0NAQ\nPXv2pPv2OEBTLygvY2NjfPbZZ/Dw8ADweAL1pqJPKBTCzc2t3bfp4uKCpKSkdl8vIYSQtmnqzpmS\nkkKTrROFQ8WeCvLw8KCWPQ5QN07llpycjJs3b8LPzw/R0dEYOHAggPYfnKWJo6MjUlJS2n29hBBC\n2s7BwQHr1q3D+vXr6UIcUShU7KkgGpE860cKAAAgAElEQVSTG9SNU7lNmTIFampq2LdvHwYOHIir\nV6/i7NmzcHJy6rBiLycnBxUVFe2+bkIIIW23YsUKuLm54a233oJEIuE6DiEAqNhTSZ6ensjOzkZh\nYSHXUVQKtewprz///BPJycmYNWsWjI2NZc+PGTMG8fHx8PHxafdtOjk5AQC17hFCiIIQCATYt28f\n4uPj8dVXX3EdhxAAVOyppL59+wIAEhISOE6iWqhlT3nNnj0b6urq2LFjxxPL+Hw+NDQ02n2b1tbW\n0NbWpukXCCFEgTg6OmL16tVYs2YNXYwjCoGKPRVkYmICMzMzGrZdzmiAFuX0448/IicnB++//z40\nNTXltl01NTXY29vTlwlCCFEw77//PhwdHbFgwQIwxriOQ1QcFXsqyt3dnYo9OdPU1ERdXR3XMUg7\nW7ZsGfT19bFhwwa5b9vJyYmKPUIIUTBCoRA//vgjoqOj8f3333Mdh6g4KvZUFBV78kfFnvJZt24d\nysrK8Nlnn3GyfUdHR+rGSQghCsjd3R3//e9/sWLFCuTk5HAdh6gwKvZUlLu7O1JSUqhboRxRsadc\nGhoasGXLFlhaWmL+/PmcZHB0dMTDhw9RXV3NyfYJIYS0bMOGDTA2NsY777zDdRSiwqjYU1Hu7u5o\nbGykLmByJBKJUFtby3UM0k4WLFiA+vp67N69m7MMTk5OkEqlSE1N5SwDIYSQp9PS0sKePXvw22+/\n4eTJk1zHISqKij0V1bt3b2hqalJXTjmilj3lUV5ejv3798PZ2RljxozhLIetrS00NDSoKychhCio\nESNGIDg4GAsXLkR5eTnXcYgKomJPRQkEAjg5OVGxJ0eamprUsqckpk+fDolEgoMHD3KaQyAQwNbW\nllroCSFEgW3btg0SiQQffPAB11GICqJiT4XRIC3yJRKJIBaLIRaLuY5CXsL9+/dx/vx5DBs2DO7u\n7lzHgZ2dHdLS0riOQQghpAVGRkbYunUrdu7cicjISK7jEBVDxZ4Kc3d3R1xcHNcxVEbTHGzUlbNz\nmzJlCng8Hg4dOsR1FACAjY0N0tPTuY5BCCGkFcHBwQgICMCcOXNQX1/PdRyiQqjYU2Hu7u4oKSlB\nbm4u11FUAhV7nd/169cRExODSZMmwdLSkus4AKjYI4SQzmL79u3Izs7Gli1buI5CVAgVeyrMzc0N\nAKgrp5yIRCIAoPv2OrGgoCAIBAL88MMPXEeRsbGxQXl5OUpLS7mOQgghpBXW1tb48MMPsWnTJhpY\ni8gNFXsqrEuXLujevTsVe3JCLXud2/Hjx5GRkYFFixZBR0eH6zgyNjY2AECte4QQ0gm88847cHFx\nwYIFC8AY4zoOUQFU7Kk4d3d3JCQkcB1DJVDLXue2YMECaGlpYevWrVxHacba2hpCoRAZGRlcRyGE\nEPIMfD4fP/zwA65du4Y9e/ZwHYeoACr2VByNyCk/1LLXeX399dcoKirChx9+CD6fz3WcZgQCAbp3\n704te4QQ0km4ubnhnXfewYoVK5CTk8N1HKLkqNhTce7u7rh79y61NskBFXudk1QqxZo1a2BiYoIV\nK1ZwHeepaJAWQgjpXDZs2AATExP897//5ToKUXJU7Kk4d3d3SCQSulFYDqgbZ+e0dOlSVFVV4auv\nvuI6Souo2COEkM5FJBJhz549OH78OE6ePMl1HKLEBFwHINyysbGBlpYWEhIS4OnpyXUcpUYte4ot\nNDQUDQ0NGDdunOy5mpoafPvtt+jVqxemT5/OYbrW2djY4OzZs1zHIEokLS0NlZWVzZ6rra1FXl4e\nYmJimj1vZWUFExMTecYjRCmMGDECwcHBWLhwIYYPHw4DAwPZsvr6epw8eRJTp07lMCFRBtSyp+L4\nfD4cHByQlJTEdRSlp6GhATU1NWrZU1B//vknxo8fj8GDByM6OhoAMGvWLDQ2NirUVAtPY2Njg5yc\nHNq3SLv5+eef0a9fv2aP9PR07Nmz54nns7KyuI5LSKe1bds2SCQSrFmzRvZceHg4HB0d8cYbb0As\nFnOYjigDKvYInJ2dqRunnGhqalLLnoK6c+cOAODGjRsYPHgwRo8ejePHj8PT0xM+Pj4cp2udjY0N\nGGN48OAB11GIknjeluxevXrBw8Ojg9MQoryMjIywdetW7Nq1C6dOncLUqVPh5+eHzMxM1NXVyc5N\nhLwoKvYInJycqGVPTqjYU1yJiYkAAIlEAgAICwuDVCqFjY0N8vLyuIz2TD179gQAKvZIu3F0dISz\nszN4PF6Lr1FXV8esWbPkmIoQ5RQUFARPT09MmzYNx48fB/D4XCQQCHDt2jWO05HOjoo9AmdnZ2Rl\nZaGiooLrKEpPU1OTutopoOrqahQUFDR7TiqVAgBOnDiBnj174v3331fYY0RXVxe6uro0hDdpVyEh\nIa1ONdLQ0IBp06bJMREhyic2Nhb9+vVDTEwM6urqnui2ScUeeVlU7BE4OzuDMYa///6b6yhKT0tL\ni4o9BfT333+DMfbUZY2Njaivr8fnn38OGxsbhZ2X0tLSErm5uVzHIEpk+vTpspbuf+PxeHB3d4e9\nvb2cUxGiHMrKyvD222+jX79+uHPnjuwC4z+JxWJERkZykI4oEyr2CHr06AFtbW3qyikHIpEINTU1\nXMcg/5KcnAw1tdb/HEqlUixZsgTu7u5yStU2FhYWVOyRdtW9e3cMHDjwqccGn8+nLpyEvISff/4Z\nu3fvBvD4omJL0tPTFbZXCekcqNgjUFNTQ+/evWmQFjnQ1tZGdXU11zHIv6SkpEAoFD51GY/Hg5qa\nGnbu3Il169bJOdnzo2KPdISZM2c+9b49iUSCwMBADhIRohyWLFmCM2fOQCQSQSBoeSY0xhhu3rwp\nx2RE2VCxRwA87spJLXsdj4o9xZSUlISGhoYnnldTU4NAIMAvv/yCBQsWcJDs+VlaWtI9e6TdPa2g\nU1NTw5AhQ2BpaclBIkKUx5gxY3Dt2jWYmZm1eMFRXV2d7tsjL4WKPQKAij150dbWpm6cCig+Pv6J\ne/b4fD40NDRw7ty5TtGCQS17pCMYGxtj1KhRzQZq4fF4CAkJ4TAVIcrD1dUVcXFx8Pb2fuqASGKx\nGFFRURwkI8qCij0C4PH0C9nZ2dQvvINRy57iaWhoQHZ2drPnhEIhDAwMEBkZCT8/P46StY2FhQUK\nCwtbvfeDkBcRHBzc7GIIj8fDxIkTOUxEiHIxMjJCWFgYli9f/sQyqVSK6OhoDlIRZUHFHgHw/0fk\nTElJ4TqKUqNiT/GkpaU1G3FQKBTCzMwM165dQ9++fTlM1jaWlpaQSqXIz8/nOgpRMhMmTJB1MRMI\nBBj9/9i77/imyv0P4J+M7jbpppu2tLULKJSylCGrDIsisoeL7WCLqKhwhQsqIJehwOUqqCAiol5B\nEGQPGQXKaCuFQveSbrqb/P7g11wKbZOWpCdJP+/XixclJ8n5nOT0Id88z3megQNhb28vcCoi4yKV\nSrF8+XJs2rQJUqm0Vi9ffn4+bt++LWA6MmQs9ggAZ+RsLiz29E9sbKxqAgqpVIqgoCBcuHABfn5+\nAidrHDc3NwDgUE7SOhsbG0RFRUEikaC6uhrjxo0TOhKR0Zo8eTKOHj0KW1tb1ZcsYrEYZ8+eFTgZ\nGSoWewTgfkMSGBjIGTl1jMWe/rl27RqUSqVq0omTJ0/C2dlZ6FiN5ubmBrFYzElaSCfGjRuH6upq\nmJmZISoqSug4REbtySefxMWLFxEYGKgq+FjsUVPVP9crtTicpEX39K3YO3DgQIu/TvObb74BAHTo\n0AGTJ0/Gb7/9JnCi2kJCQhASEqL2fiYmJnBwcGiRPXtFRUV6974Zm8rKSlhYWKBjx47Yu3ev0HGM\nmkwmw8CBA3X2/Gz3Dcf8+fOxfv16nD17Fr/88gu6desmdCTSc5GRkZDL5bVuY7FHKsHBwTh8+LDQ\nMYyavhV7s+fMQRx7cwEA0dHRejk8bdGiRViyZIlG93V1dUVWVpaOE+mf1NRUjBo1SugYLcKpU6dw\n6tQpoWMYNf+AJ3Djr3idPT/bfcOUmJjIdo7UunLlCtq2bVvrNhZ7pBISEoLU1FTk5+fD1tZW6DhG\nSR+XXhgxYzZGvzlf6BiCUCoU+O/WTRj6sn6uoTcn6ulG3d/W1hb5+fk6SqP/Vv3yB1oHBAkdw2jd\nuhYD78BgSKR1rwdGj2/X+lWIPvCLzvfTktt9Q3X+8O9w8/aFu69hXU9OzSP1VgJmDulV5zZes0cq\nwcHBAMAZOXVI33r2WjqRWKy3hV5TyOVyDs8inWkT2p6FHpFAIvoMYKFHTcJij1RqZuTkJC26Y2Vl\nhaqqKlRUVAgdhYyQXC5v0T17REREVBuLPVKpmZGTk7TojpWVFQCwd490gj17RERE9CAWe1RLSEgI\ne/Z0iMUe6ZKtrS2LPSIiIlJhsUe1BAQEICEhQegYRovFHukSh3ESERHRg1jsUS3+/v64c+cOysrK\nhI5ilFjskS5xGCcRERE9iMUe1RIQEACFQoHExEShoxglFnukS3K5HIWFhVAoFEJHISIiIj3AYo9q\n8ff3h0gkwo0bN4SOYpRqij19W2uPjINcLodCoUBxcbHQUYiIiEgPsNijWqysrODu7s5iT0csLS0h\nEon4YZx0Qi6XAwCHchIREREAFntUB07SojtisRgWFhYs9kgnbG1tAYCTtBAREREAFntUBxZ7uiWT\nyVBYWCh0DDJC1tbWAMAvE4iIiAgAiz2qg7+/P4dx6hCLPdIVExMTAEBlZaXASYiIiEgfsNijRwQE\nBCAjI4MFiY7Y2NigqKhI6BhkhKRSKQCgqqpK4CRERESkD1js0SP8/f0BgEM5dUQmk7HYI51gzx4R\nERE9iMUePcLX1xcmJiYcyqkjHMZJusKePSIiInoQiz16hImJCby9vdmzpyMs9ppuz+b1GB8egFef\nbK+15zy59ydM79sFo9t549KJI3Xe9vWnH2HRhOe1tk9dqenZY7GnXlPe07rOleZy/dxpTO/bBcMD\n3RAXfU51+/hOT2DP5vWqn3d/sUYr+9PVOf/d2k8xtoMfpvXprLqtMceg6X110VY0tO+tK5ZgZIgX\n3p8wHMWFXPpEm3TxXjZk5aypeCHYA1tXLGmW/TVVc7cJuqKL3399JGT7wWKP6sQZOXXHxsaGxV4T\nDZv8Gl56+0OtPV9xYQHWvTMbsz5dj7fW/QdlJffqvM1Q1PTscRin9gl9Xuz41ydw82mDby8mICj8\nf4WSo4sbnNw8AADObh6qn/XV6DfmYcK8d2vd1phj0PS+2m4r1O37xQXv41+/Hcft+Os48uNOre63\npdPFe9mQuZ9tRHB4l2bbX1MZS5ugi99/fSRk+yHV+jOSUQgICMCpU6eEjmGUeM3e4xGJRFp7row7\niagsL4dHmwBYyWQAgIQrlx65rVvkM1rbpy6JxWKIxWL27Glgwrz3GnX/us6V5pSXnYXIMRNhbmlV\n63YnN3c4uXv8/8/a+/DT2NfncTTmGBpz38dtK6qrq3Dspx8QHNEVLl7eavft4uWN1gGByMvOeqz9\n0qO02e4bC220CQ+f40LQ1e+/vhGy/WCxR3Xy9/fHf/7zH6FjGCVD7tn7bu2n2P3FGsjsHCAWi1CY\nlwuxRAIPX3+UFBeh4O7fqK6uQq+hwzFtyScAgJz0VPx7ybu4du40TM3NEdypKyYt+gh2Tq002p5/\nNwdfLJqPK6dPQKFUwNLaBiJo/h//L19+gf9+uREFubmwc3LGiwveR/eBUTi9/7/YvOQdAMDEzoEA\ngNkrN2DL0kW1bpuz6gt88cFbMLe0xOZjF7F99XL8uHkdhk16DeZWVjj0/bfwDgzGhaOHGvW66IqJ\niQl79tT48/e9WP/uXNV7uvuLNfhu7afoPvAZiMUSXDz2B0wtLPDGP9egXfcedZ4r319PRm5WZr3n\nri7OE7FY8shtTm4ecHJ1v/+zuwcc3dxV2+o799Ud78OvT1N+7+vbd10aOoYdaz7GsZ9/wGe/HoG5\npVWD91XXVqhra1Ju/oUtSxfh5tUYKBUKmFtYosuAwegzfLTanA++R0qlss7jNFSG1u6vf2c2juz5\nHhPmv4dnX5mOX7duxvbVy+HRxh8f796v9vyvkZWahM/mvYZLJ47AxNQMMz76FB179QVQ//m9c91K\n7Fq/CiGduyMnPRX5OdnwaxuGGUtXwsXLW+32lbOn4fRvv8DM3AKjZ87H0JenYcN7c3H0px/gHRiM\nj3/4rdaxNrZNaOgcr6vNWrD+S1jJZPW+V+pea3XHqy6vpr//6vbzuDm18b4J2X5wGCfVKSAgAAUF\nBcjOzhY6itEx5Gv2Rr8xDyGdu6Fdt6ew+tej2HYuDmFP9UZJcREW/Xs7vvrzOl5f9hkOfv8tMpJu\nAwBWzp4GkViE9b+fwie79+NeUQFWvP6q6jnVbV82dSKK8vOwZt8xfHsxAcMmv96ozHczM/De5u34\nNvoGug+MwraP/wEA6D4wCm+t3QIA2HYuHrvj0/HUkOceue3JwUMxfu47qucbO/ttBId3QcLVSwjq\n2BmDxr+Mka83/nXRFalUyp49NboOGFLrPR0+bSaCOnaGpbUM0/7xCTYevYDWAUH4fv1KAHWfKxKJ\ntMFzV9vnSXlpKUTiR//LnvLhcti3cgEATFq0tNa3xfWd++qO9+HXpym/9/Xtuy4NHYNSoYBSqVR9\nAGrovuraCnVtzeq5MyC3d8TmoxewZu8x2NjZo7KiXKOcNURica3HGANDa/dfW7Yart6+qn8/8+Jk\nPD/1DdW/1Z3/NeT2jpj+0afYdCwaT4SF47u1n6q21Xd+j3p9Llo/EQRPvwCs/uUPrPv9NMRSCT7+\n/2NTt33u6i/g5u2LbgOfwdCXpwEAZny0EgFhHbH0259q5WtKm9DQOV5Xm6VUKht8r9S91uqOV11e\nTX//1e3ncXNq430Tsv1gsUd1CggIAADOyKkDhlzs1bCxs4e1TA4zC0t07NkHZSUlcPHyhtTEBB17\nPg0AyM3OxO3Ya0iIuYgxsxbA1tEZjq7uGDd7IRJiLuJ27DW1229di8GtazGYOH8RnNw8IJFIYWFl\n3aisLy9cjNZPBMHEzAwBYR2RfzdHK6+BT2AogiO6YujL0+ATFNKo10WX2LPXdOaWVjAzt4C5pRU6\n9OyDu5kZ9d5X3blb43HPk+qqShz/ZTcKcv9GQPuOjToeded+Y463sbm19Xs3dvbb2HjkvNrfe3Vt\nhbr3q7q6Csk34tG570BYWNvA0dUNQeGdkZl8p1F5n+jQCef+2I/khHij6+EzpHZfE+rO/5rtZuYW\nCOvRu9Z2dee3raMTzCwsYe/cCmNmLkDSjbha7UJD2/uNGIszB/aqrgu+Ex8LL/9AmJiZAWh6m6Dp\nOf5gmwWlUqN2Th11r0d9NP39f9z9aPr4x3nfNKGr9oPFHtXJw8MDVlZWnKRFB2xsbFBaWmo0H8hF\nIjHwQKOk+qZRqURmyh0AgItna9V2D18/AEBmyh2129PvJAIAvAODm5zv5y0b8Hrkkxgb1gYrZ02t\nlVWXGnpddIk9e9ohFosb/M9W3bmrKXXnyTcr/4k1b72BEdNnwb9dB42fF2jcua/ueBubu7l/79S1\nFereL4lECid3T5z7Yz9Ki4vwd0Y6rp8/A5+g0EblGDF9Fhxd3DA7qg/iL55vwpEYBn1v9xtL3fn/\n8PE25vx292kDAMhKS9Zo+9PDRqGqshJnDvwKADj+y270/f+hxEDT24SmnOPaaucepO710JbH3Y+2\n3zdN6Kr9YLFHdRKJRGjTpg2LPR2Q/f/kDi1pkpYH/xNVKO7//OAF9/Vtr6qsAABIpCZN2u/1c6fx\n9adLMW7OQmw5eRlzVn3epOcxJFKpFNXV1ULHaDHUnduPa9ychXhj+WfYuW4l4i6c1fhxQp77Quxb\n07aiofdr5sdrEX3sD7zYNRTznx8Av7ZhGPPm/Ebl+Paz5cjJSMOqn/9AYMeIRj3W2AjV7utaY8/v\nqqr7X+xKJHVPk/Hwdpm9Azr3jcSRPbugVCiQnBCPNqH/W3aiqW0C0PRzXJvtnLrXQ1sedz/aft80\noav2g8Ue1SsgIIDDOHWgptgz9KGcmnDx8gEAZKX875uxtNsJqm3qtju63L+AOSMpsUn7T72VADvn\nVugW+QwsrG3qvL7B2FRWVqqWYCDdUXfuaovUxAS9nxsJW0dnJMZpPhxJyHNfiH2rayvUvV9KpRL7\nt3+FcbPfxvfXkvDlmWt4c8W/YGFt06gcidevonPfgWj9RFCLnUFS6HYfuF+IKBW66U1u7PmdcuMv\nAIBr67rbhbq29x85DrHnz+DwjzvR/slete7f1DahKee4Ju1cY19rda+Htjy8H23nbOz7pgldtR/G\n/8mHmozFnm60pJ49n6AQBLQPx441K5D/dzb+zkjH9tUrENA+HN6BwWq3B0d0gYOLK7auWKLaHn30\noMb7t3N2QWHuXaTeSkBxQT5uXYvR4dHqh4qKCpiamgodw+ipO3e1zdzSEkqFQuP7C3nua3PfO9Z8\njGl9Oqtd11BdW6Hu/RKJRDAxM8O///Euhge6YXigG0a1bY13xz6LnPRUzQMrlTA1M2/q4RoFodt9\nALBzaoUrZ06grOQe7hUWIi9He5PNaXJ+l5WWoLqqEn9npGHHmhUIiegGjzb+Gm9v170nnNw8sPXj\nxeg1dHidORrbJjTlHNekndPktVZ3vPXR9Pdfk/1oI6c23rcG6aj9YLFH9fLz88OtW7eM7iJzodnY\n3P8WzRB79nauW4nr587g951fY+e6lTi59yds/XgJ8u/mYP7zkaq/AeCz+a8jM/kOZq/agOrqaszo\n1w1vDu4JU3NzzF61QfWcDW2XSE2w8PNtuFdYiBn9u+OjyeNgaSNH/t0crJ47Q23eDj2eRtiTvTF/\neCQ+ePEFmFlYorKiAkteHYObVy9j9dzpAIA5z/bFb99+Wedtu79Yg20f/wO5WZmYM7QvPl80D3EX\nz+G3b/+D9e/OafLroiuVlZUwMdHP4U/64uH39D9LFyHu4jns3/EVft/5NS4e+wPbP1uBnPRUfL5o\nXp3nBdDwubv9sxVaP08UCs2H5zZ07u/e+K8Gj/fh12fTh283Krdba596971r/Sp8s3IZctJTMefZ\nvijMvftY72UNTdoKdW0RlEqs/OkQdsenY3d8Or65cAOWNjLs2bRO4xwKRbXR9egZWrsP3F+IPSk+\nFpN6dsS6hTNRXJCPxLhr2PLRe2rP/5rf3ZrtV06fwNef/AP5d3Ow5q03GvzdqnHwu68xtoMf3hjU\nA3JHJ8z71+Za+dRtF4lE6PvCGIR2eRIye4d6j7MxbQKABs/xutosQP3vTUOvtabHqy0N7UcbObX1\nvtVHV+2HSMlP8lSPI0eOoE+fPsjKyoKzs7PQcYxGcXExbGxssG/fPgwaNEjQLMEhIQjtHYnRjbwu\nhZrHnKinMXH0SCxZskTjx0ilUnz99dcYM2aM+jsbibi4OAQHB2PVL3+gdUCQ0HF0YtGE52FqZo75\n/9r8yCLK9PgSYi5i3btzsObXo7VuX7dwNkSi+1O3q5OZfAfzhw/EyNfmIOqlKU3KsWv9KkQf+AU3\n/vqrSY/XBNt93Zr7XD90HzQUw6e+2aTtNfZsXg+foBCEPdW7zu2NbRO0cY43habHK/R+mut9q8/j\nth+ptxIwc0gvXLlyBW3btn1wUxx79qhe3t7eAIA7d+4ImsPYWFlZQSKRGGTPnj7JTktRDUWp7092\nWorQMZuVQqFAdXU1e/aM0NiZC5B++xbGdfRHXPQ5oeMYHVNzC2SnJuPqnydRWVGBwty7OPDdNpz4\n9Ud0HzRU7eO3rliCNwf1hE9QKPo837gZ+EhzhtLuqxteWd/2wz/uRGlxES6dOILYC382WDA0tk14\n3HP8cTRmuKmQ+2mO960uum4/eBU/1cvT0xNSqRR37txB586dhY5jNEQiEaytrVvENXu65Ozuid3x\n6ULH0Cs1y3mw2DM+QZ264PM/GjfzHmmu9RNBeOntD7Fp8ULkpKdCIpHCJzgU89f+Gx16PK328S8u\neB8vLni/GZK2bPre7m9e8g6S/opD+p1E5P+dg0mLljZqe0LMRXzx/lsI7BiBWZ80PHy4sW3C457j\nTaHuePVlP835vtVF1+0Hh3FSg7y9vTFjxgy89dZbQkcxKl5eXpg1axbmzJmj/s46xOE8+q2xwzj1\naYhwc2oJwzipZeAwTiJqCg7jpCbz9vbmME4dkMlkHMZJWseePSIiInoQiz1qEIs93ZDJZBzGSVpX\nUXF/MWIWe0RERASw2CM1WOzpho2NDXv2SOvYs0dEREQPYrFHDWrdujXu3LnDtfa0jD17pAs1xR4X\nVSciIiKAxR6p4e3tjdLSUmRnZwsdxajwmj3SBfbsERER0YNY7FGDuNaebrDYI12o6S22suKi20RE\nRMRij9R4cK090h5es0e6UHNOyWQygZMQERGRPmCxRw2SSqVwd3dnsadlNjY2vGaPtI7FHhERET2I\nxR6p5e3tjaSkJKFjGBUO4yRdKCwshKmpKczNzYWOQkRERHqAxR6pxeUXtI/FHulCUVERe/WIiIhI\nhcUeqcViT/tkMhmqqqpQUlIidBQyIoWFhSz2iIiISIXFHqnFtfa0z8bGBgB43R5pFXv2iIiI6EEs\n9kgtrrWnfTUfyDmUk7SJPXtERET0IBZ7pBbX2tM+FnukCyz2iIiI6EEs9kgtrrWnfSz2SBdY7BER\nEdGDpEIHIP3Htfa0T5+u2Uu5eQOnf/tF6BhUh5Li4kbdv7CwEC4uLjpKo/9iTh1D2q0EoWMQNVny\nzRvNsh+2+wCUSkAkEjoFkVbczcqsdxuLPdII19rTLjMzM5iZmQnesycSifDn73vx5+97Bc1B2lFU\nVKT6IqEl2rpiidARiB5bwBNP6PT52e4TtSws9kgj3t7euH37ttAxjIpcLhe82Lt+7Zqg+yft+vvv\nv2Fvby90jGYXFBTE2YKb0VdffZL7E30AACAASURBVIXp06ejtLRU6CjUBC2x3S8qKsK2bduwfv16\nxMXFoUePHpg9ezaGDRsmdDQinWOxRxrx8vLChQsXhI5hVORyOfLy8oSOQUYkOzsbzs7OQscgI+fq\n6oqysjLk5+fD1tZW6DhE9UpISMCWLVuwadMmlJaWYsSIEdixYwfat28vdDSiZsNijzTi5uaGtLQ0\noWMYFTs7O+Tn5wsdg4xESUkJ7t27x2KPdM7V1RUAkJmZyWKP9I5CocDhw4exZs0a7N27F76+vliw\nYAEmTZoEBwcHoeMRNTvOxkkacXd3R35+Pu7duyd0FKNhZ2fHnj3SmqysLABgsUc6V1PsZWRkCJyE\n6H/y8/OxZs0atGnTBpGRkSgrK8POnTvx119/YcGCBSz0qMVizx5pxN3dHQCQnp4Of39/gdMYBxZ7\npE3Z2dkAWOyR7jk6OsLExITFHumF6OhobNq0Cd988w1MTEwwatQozJo1C0FBQUJHI9IL7NkjjdQU\nexzKqT0s9kibWOxRcxGJRGjVqhWLPRJMRUUFdu3ahf79+6NTp044fvw4li1bhrS0NGzcuJGFHtED\n2LNHGnFycoKpqSmLPS1isUfalJ2dDWtra1haWgodhVoAV1dXFnvU7DIyMrBx40Zs2LABubm5GDRo\nEA4ePIh+/foJHY1Ib7HYI42IxWK4uLiw2NMiFnukTZyJk5qTi4sLiz1qNidPnsS//vUv7NmzB46O\njnjllVfw2muvwdPTU+hoRHqPxR5pzN3dncWeFtna2rLYI63JyclhsUfNxtXVFQkJCULHICNWVFSE\nHTt2YN26dbh69SrCw8OxZcsWjBkzBiYmJkLHIzIYLPZIYyz2tMvOzg5FRUWoqqqCVMpfRXo87Nmj\n5uTq6ooTJ04IHYOMUF1r423btg1hYWFCRyMySJyghTTGYk+77OzsoFQqUVBQIHQUMgIs9qg58Zo9\n0iaFQoFDhw4hKioKTzzxBH744QcsWLAAqampLPSIHhOLPdKYu7s70tPThY5hNOzs7ACAQzlJK1js\nUXNydXVFfn4+SkpKhI5CBoxr4xHpHseOkcbc3d2RkZEBhUIBsZjfEzyummIvPz9f4CRkDLKysljs\nUbOpWVg9KysLPj4+AqchQ/Pg2nhSqRSjR4/m2nhEOsJP7KQxFxcXVFZWIjc3V+goRoE9e6Qt5eXl\nyMrKgpeXl9BRqIWoKfY4lJM0Vd/aeOnp6Vwbj0iH2LNHGmvVqhUAIDMzE46OjgKnMXwymQwSiYTF\nHj221NRUKJVKTkNOzaZVq1YQi8Us9kgtro1HJCwWe6QxFxcXAPeH7YSGhgqcxvCJRCLI5XIWe/TY\nUlJSAIDFHjUbExMTODg4sNijenFtPCL9wGKPNGZvbw8TExNkZWUJHcVocGF10oaUlBSYmZnxmj1q\nVlxYnR7GtfGI9A+LPdKYSCSCs7Mziz0t4sLqpA3Jycnw8PCASCQSOgq1IK6ursjMzBQ6BukBro1H\npL9Y7FGjtGrVisWeFrFnj7QhJSWFQ6Oo2XGtvZZNoVDg8OHDWLNmDfbu3QtfX18sWLAAkyZN4pIJ\nRHqEs3FSo7Rq1Yrf5GqRnZ0dl16gx5aSksKZOKnZsdhrmbg2HpFhYc8eNQqv0dAuOzs7JCYmCh2D\nDFxKSgo6dOggdAxqYVjstSxcG4/IMLHYo0Zp1aoVLl++LHQMo8FhnKQNHMZJQnB1dUVOTg6qqqog\nlfLjhDGqqKjAzz//jE2bNuHQoUMIDAzEsmXLMGnSJFhZWQkdj4g0wNaZGoXDOLWLxR49ruLiYuTn\n57PYo2bn6uoKhUKB7OxsuLm5CR2HtCgjIwPbtm3D2rVrkZmZqVobr2/fvpwIisjAsNijRmnVqhVy\ncnKgUCggFvOSz8fF2TjpcXGNPRJKzdqrGRkZLPaMxINr4zk4OOCll17i2nhEBo7FHjVKq1atUFVV\nhdzcXDg6Ogodx+DZ2dmhoKCAxTM1WXJyMgAWe9T8ago8Xrdn2Lg2HpFxY7FHjVJT4N29e5fFnhbY\n2dlBoVCgsLAQtra2QschA3Tz5k04ODjw/KFmZ2lpCRsbGxZ7BurBtfFKSkowcuRIro1HZIRY7FGj\n1EypfPfuXYGTGAc7OzsAQF5eHj+sU5PcunULfn5+QsegFoozchoWro1H1PJw3Bg1Cos97Xqw2CNq\nips3b7LYI8G4urpy0i4DwLXxiFou9uxRo5ibm8PS0pLFnpaw2KPHdfPmTYwYMULoGNRCsWdPv128\neBEbN27k2nhELRiLPWo0BwcHFntaIpfLIRaLkZ+fL3QUMkAKhQK3b99GmzZthI5CLZSrqytOnz4t\ndAx6ANfGI6IHsdijRmOxpz0SiQQ2Njbs2aMmSUlJQVlZGYdxkmDYs6c/uDYeEdWFxR41Gos97eLC\n6tRUN2/eBAAWeyQYFxcXZGZmQqlUsqAQCNfGI6KGsNijRmOxp10PLqx+79495ObmIjc3F/b29vzP\nmhp08+ZNyGQyODs7Cx2FWii5XI6Kigr8+uuvKCoqQnZ2NtLS0lBcXIzPP/9c6HhGq7i4GNu3b+fa\neESkFos9ajQHBwfExcUJHcMg7d+/H4cPH0Zubi7u3r2LrKwsZGRkYMOGDVi5ciUqKipU9923bx+L\nPWrQrVu3eL0eNZvKykpMmDABiYmJyMjIQHZ2tqrNGjp0KEQiEUxMTFBVVYXevXsLG9ZIcW08Imos\nFnvUaA4ODsjNzRU6hsH65JNPIJFIUF1dXe99TExM0LNnz2ZMRYaIyy5QczIxMYGpqSkuXLgApVL5\nyHalUomKigqYmpqiT58+AiQ0Tg+vjefj48O18YhIY1xnjxqNwzibLjIyEn5+flAoFPXeRyQSoWvX\nrpw1jdTigurU3BYuXKj2PhUVFejVq1czpDFu9a2Nd+PGDa6NR0QaY7FHjWZvb8+evSYSiUSYPXt2\ngxMZSKVSDB48uBlTkSFSKpUcxknNLigoCJGRkQ1eF2ZqaopOnTo1YyrjcvHiRUydOhXu7u54//33\nMWDAAFy9ehUHDx7EiBEjIJFIhI5IRAaExR41mkwmQ2lpKSorK4WOYpBeeumlBnvtKisr0b9//2ZM\nRIYoKSkJ9+7d4+LI1OwWLlxYb/svEokQEREBc3PzZk5l2CoqKrBr1y70798f4eHhOH78OJYtW4b0\n9HRs3LgRwcHBQkckIgPFYo8aTSaTAQAKCwsFTmKYLC0tMWXKlHq/GZfL5ejQoUMzpyJDExsbCwAs\n9qjZ9ezZExEREXX2MJmYmKBfv34CpDJMGRkZWLFiBXx9fTFmzBiYm5vj4MGDiI2NxcyZMzmcn4ge\nG4s9ajQWe4/vjTfeqHOCFqlUigEDBkAs5q8mNSw2NhZubm6ws7MTOgq1QAsXLqyzDauoqGhxk0uV\nlpY2+jEnT57EyJEj4eXlhdWrV2P8+PG4ffs2/vvf/6Jfv35cs5CItIafKKnR5HI5AKCgoEDgJIar\ndevWiIqKeqR3T6lUIjIyUqBUZEji4uI4tIsE89xzz8HPz++RL6akUim6du0qUKrmFx0djbCwMBQX\nF6u9b3FxMTZt2oR27dqhR48eSExMxPr163Hnzh0sX76cS+0QkU6w2KNGY8+edsyaNeuR616qq6t5\nvR5pJDY2lsUeCUYkEmHBggWP3N6xY0dYWloKkKj5HTx4ED179sSNGzfw7bff1nu/hIQEvP322/Dy\n8sKbb76JsLAwXLp0CRcuXMCUKVN4fSMR6RSLPWo0Fnva0bt3bwQHB9f6ZtzX1xdeXl4CpiJDERcX\nx+v1SFATJkyoNf2/qalpi7leb/fu3RgyZAjKysogEomwatWqWmsPKhQKHDp0CFFRUXjiiSewa9cu\nLFiwAGlpaVwEnYiaFYs9ajQLCwuYmpqy2NOCuXPnqn42MTHBkCFDBExDhiItLQ0FBQXs2SNBmZmZ\nYe7cuZBKpQBazvV6a9euxYgRI1BVVQWFQgGlUokbN27gxIkTqrXx/Pz8uDYeEekFFnvUJDKZjNfs\nacG4ceNga2sLAKiqquIQTtIIZ+IkfTFt2jSYmpoCACQSCbp37y5wIt1RKpX44IMP8Oabb0KpVNbq\nyZNKpXj99dfh6uqKDz/8EMOGDcNff/3FtfGISHAs9qhJZDIZe/a0wMzMDNOnTwcAiMVi9O7dW9hA\nZBBiY2Ph6OgIJycnoaNQCyeXy/Haa68BANq2bQsbGxuBE+lGdXU1Jk2ahI8++qjO7VVVVbh+/ToW\nL16M1NRUrFy5En5+fs2ckojoUVKhA5BhksvlBlvsxcbG4tq1a0LHUPHw8IBYLIavry9+++03oeM0\nKDIyUjUbKwknLi4OISEhQscgI5aXl4fq6moUFhaivLwcJSUlAICioiJUVVXVum94eDgkEgk8PT2x\na9euWtusra3rXFPU1tYWIpEIUqkUNjY2MDc3h4WFBaysrFQ9hfqipKQEw4cPx8GDB6FQKOq9n0Qi\nQUlJCdfGIyK9IlI+OA6BSEO9e/dG27ZtsXbtWqGjNNqSJUvwwQcfCB3DIF25cgVt27YVOkaL17Nn\nT4SGhmLDhg1CRyE9lJOTg5ycHGRnZyM3NxcFBQXIz89Hfn5+nT8XFhaipKQE5eXldRZzQpDL5ZBI\nJLC1tYW5uTlsbW1ha2sLuVxe62c7OzvVv52cnODs7AwnJyetFYy5ubkYPHgwoqOjNXpdHBwckJ6e\nrncFKxG1WHHs2aMmsbGxQVFRkdAxmszT1w+f7TsudAyVW9dioKiuhn/7jkJHqVPqrQTMHNJL6Bj0\n/2JjYzFy5EihY1Azqq6uRmZmJpKSkpCSkoK0tDSkp6erirqMjAzVzw8XJdbW1nUWSx4eHpDL5ZDJ\nZKoeNZlMBolEAjs7O0gkEshkMpiamqp6q2p64B6WmpoKDw8P2NnZ1bo9Pz8fD3+nXNNjCNyf1OXe\nvXsoLS1FWVkZiouLUVlZicLCQlRXVyMvLw9lZWW1itP4+HjVv/Py8urch4ODg6rwc3V1Vf3s6emp\n+uPl5dXgsgd37txB3759kZKSonEBnJubix9//BGjR4/W6P5ERLrGYo+axNzcHKWlpULHMBptQts/\n8mGFqC7Jycm4e/cu2rdvL3QU0iKlUonU1FQkJCTgxo0bqqKu5u/09HTVupwSiQQuLi5wc3ODs7Mz\nXFxc0KFDBzg7O6v+3apVKzg5OcHe3l41W6YuPVzk1aiZgOphjo6OWt1/QUEBsrOzkZ2djZycHGRk\nZKh+Tk9Px6VLl5CdnY2UlJRa/3c5OzvXKv68vLzg5+cHpVKJKVOmICcn55F9icViSCQS1bI5CoVC\n9d4olUqsX7+exR4R6Q0We9QkFhYWyM/PFzqGURGJREJHIAMQExMDkUiEdu3aCR2FmqCoqAjXr19H\nfHy8qrBLSEhAQkKC6ro4W1tbeHt7w9PTE2FhYYiKioKHhwe8vLzQunVruLq6NksBZ0jkcjnkcjn8\n/f3V3jcnJwepqam1iumUlBRER0dj9+7dSEtLU91XJBLBxMQElpaWqqGinp6ecHV1faSn9MHeUyIi\nfcH/LahJLCwskJGRIXQMohbn8uXL8PHx4QdKA5Ceno7o6GjExsbi+vXriI6ORnx8PBQKBUxNTeHh\n4QFfX1/06tULM2bMgK+vL3x9feHj48Mvf3TIyckJTk5O6NChQ53bb9++jfz8fKSkpCAuLg6JiYmq\nP+fPn8f58+chl8sRGhqKkJAQBAcHIygoCB06dODkLESkd1jsUZNYWFhwGCeRAGJiYhAWFiZ0DHpI\namoq/vzzT5w5cwZnz57F5cuXce/ePUgkEvj5+aFdu3YYO3Ys2rZti3bt2qF169Ys6PSUj48PAKBD\nhw4YOnRorW25ubm4evUqrl69iitXruDy5cv49ttvVe91QEAAunTpgq5du6Jr164IDQ3lGntEJCgW\ne9QkLPaIhBETE4MJEyYIHaNFq66uxoULF3Dq1CmcOXMGf/75J1JTUyGRSBAaGoru3bvj5ZdfRvv2\n7RESElLnhCZkmOzt7dGrVy/06vW/CasUCgVu3bqFmJgYXL58GWfOnMHu3btRVFQEa2trREREoFu3\nbujatSt69eoFmUwm4BEQUUvDYo+ahMUeUfMrKipCYmIie/YEkJiYiEOHDqn+5OXlQS6XIyIiAq++\n+irCw8PRo0ePeickIeMlFovh7+8Pf39/vPDCCwDufyEQHx+P6OhoREdH48CBA1i+fDlEIhHCwsLQ\nr18/9OvXDz169ICZmZnAR0BExozFHjWJhYWFajIBImoeV65cgUKh4EyczaC0tBQHDhzAzz//jIMH\nDyItLQ0ymQy9e/fG4sWL0bdvXwQHBwsdk/SURCJBSEgIQkJCMHHiRADA33//jcOHD+PQoUP4/vvv\nsWLFClhZWaFnz54YMmQInnvuObi7uwucnIiMDYs9ahL27BE1v8uXL8PW1hZeXl5CRzFKRUVF2Ldv\nH3bv3o19+/ahtLQU3bp1w7Rp09C3b19ERERwFkxqMkdHR4wcOVK1RuaDvcULFy7Em2++ia5du+L5\n55/H888/r7p2kIjocYiFDkCGicUeUfOrmZyFE3toT3V1Nfbu3Ythw4bB2dkZ48ePR25uLj755BOk\npaXh5MmTeO+999CtWzcWeqRVvr6+mDJlCr7//ntkZ2djz5498Pf3x9KlS+Hr64tOnTph7dq1XOaI\niB4Liz1qEhZ7RM3v8uXLvF5PS1JSUrB48WL4+PggKioKBQUFWL9+PTIzM3Ho0CFMnz4dLi4uQsek\nFsLc3BxDhw7FV199haysLBw4cADt27fHO++8Azc3N7z44os4deqU0DGJyACx2KMmsbCwQFVVFSor\nK4WOQtQiVFdX4/r161xM/TFFR0dj2LBh8PHxwYYNGzBmzBjEx8fj8OHDeOWVV+Dg4CB0RGrhTExM\nMGDAAGzZsgVpaWlYtWoVrl27hqeeegqhoaH4+uuvUV1dLXRMIjIQLPaoSUxMTAAAVVVVAichahni\n4uJQUlJS70LQ1LBLly7h2WefRUREBNLT07Fjxw6kpKRgxYoVCAgIEDoeUZ1kMhmmTZuG6OhoXLhw\nAZ06dcIrr7yCkJAQfPPNNyz6iEgtFnvUJDWLxLLYI2oe58+fh7m5OUJCQoSOYlBSUlIwfPhwhIeH\nIz09Hf/9739x9uxZjBgxAqampkLHI9JYeHg4vvrqK8TFxaFLly54+eWXERISgv379wsdjYj0GIs9\napKaiQr4rSJR87hw4QI6duyo6lWnhimVSmzatAmhoaGIjY3Fzz//jHPnzmHIkCFCRyN6LH5+fti6\ndStiY2PRvn17DBo0CK+88gonciGiOrHYoyap6dljsUfUPM6fP4+IiAihYxiE1NRU9O/fH6+99hqm\nT5+OS5cuISoqirOYklHx9/fHzp07sWfPHuzfv5+9fERUJxZ71CQcxqk7ezavx/jwALz65P2Fs+c8\n2xfDA91wZM9OgZORUCoqKnDlyhV06tRJ6Ch6Lzo6Gl26dEF6ejpOnz6N5cuXw9zcXOhYai1YsAC9\nevUSOsZj0Zdj0JcczeW5557D9evX8fTTT+OZZ57BZ599JnQkItIjLPaoSTiMU3eGTX4NL739oerf\nq37+Aza2dsIFIsFduXIF5eXlLPbUuHz5Mvr164fQ0FCcOXNGr3tCDx06hHXr1gkdQ2OGlrelsbOz\nwzfffIPly5djzpw5+Pjjj4WORER6givEUpO09GGc1dVVOPbTDwiO6AoXL2+tP39dw80kUl6r1VKd\nP38eNjY2nDWyAdnZ2RgyZAg6deqEX375BWZmZkJHatDmzZvRo0cP1b9XrFghYBr1Hs5bF305hubM\nUVVVhW3btqFnz57w8/Nrtv3WZ968eTAzM8PMmTPRpk0bDB8+XOhIRCQw9uxRk7S0YZwpN//Chy+P\nxPhOT2BcR39M6RmOm9diVIVeTnoq/jntRYzr6I+Xu7fFJ29ORl5Olurx6rbn383B8hkvYWxYG4xu\n74NvV//zkQzfrvqnav8fvDgCaYk3AQArZ0/D8EA3jA1rg1++/AIAsOG9uRgZ2hpvvTAIAJCVkoQl\nr47B+E5PYHrfLvjjhx26eqlIB2qmXBeL2WTXZ8aMGTA3N8euXbuatdBbuXIl3N3dYWpqitatW2PX\nrl2qbdnZ2Rg3bhw8PDxgZmYGDw8P7Ny5E6+++ip27dqFN954AyKRCOvXr4etrS08PDxUj01KSkJU\nVBRsbGzg7OyMF154ARkZGQCApUuXQiqVYsyYMRg/fjzs7e3h4eGBQ4cOaZT53XffhUQiwTvvvINl\ny5bBx8cHMTExSExMRGRkJORyOXx8fLBlyxYAeCTvq6+++sjj33///UeOob7na9OmDUQiERwdHfHr\nr78CAN566y2Ym5vD19e3wceqs3v37lo5PvjgA0ilUri6usLd3R1mZmawtLREeHg4/P39IZPJYGlp\niSlTpmj0nl6/fh39+vWDXC6HjY0NPDw8cOHCBVWh19Tc2vTGG29gypQpmDx5Mu7evdvs+yci/cJP\nDtQkLa1nb/XcGZDbO2Lz0QtYs/cYbOzsUVlRrtq+cvY0iMQirP/9FD7ZvR/3igqw4vVXNd6+bOpE\nFOXnYc2+Y/j2YgKGTX79kQyRYybiy9NXsfa3ExBLxPjn9BehqK7G3NVfwM3bF90GPoOhL08DAMz4\naCUCwjpi6bc/QVFdjSWvjoFrax9sPnoB05Z8jM8XzUPClUs6fMVImzg5S8MuXbqEH3/8EWvXroWt\nrW2z7js1NRW//fYbCgsLMWLECMyfP1+17bnnnsPNmzdx9OhR5OfnY9myZcjIyMCWLVvg5eWFtWvX\nQqlU4rXXXsPy5ctrPe+oUaMgFouRkJCACxcuID8/H8OGDQNwv1h76qmnIJfLsWnTJiQnJ6Nt27ZY\nvHixRpmXLl2KHj164Ny5c3jqqafw+uuvo6qqCgMGDICfnx9SUlKwceNGTJ48GefOnXsk75YtWx55\n/PPPP1/rGKqrq+t9vvPnz8PCwgLz58/HM888AwD4+OOP0bNnT5w+fbrBx6ozfPjwWjkWL16M3r17\no2/fvrh27Rpyc3MRGRmJgoIC7N+/H3///Te+/PJLbN68GQkJCWrf07Fjx8LZ2RkpKSmIjY2Fo6Mj\nysvL1R5zc/v0009hamqKTz/9tNn3TUT6hcUeNUlLumavuroKyTfi0bnvQFhY28DR1Q1B4Z2RmXwH\nAHA79hoSYi5izKwFsHV0hqOrO8bNXoiEmIu4HXtN7fZb12Jw61oMJs5fBCc3D0gkUlhYWT+Sw87J\nGSamprBv5YKJ8xYhI+m2qmDrN2IszhzYi7KSewCAO/Gx8PIPhImZGRKuXkZm8h0Mn/omLKxt0P7J\nXnDx8saVMyea7TWkpispKUFcXByv12vA9u3bERAQgMGDBzf7vlevXo127drB3NwcXbt2RVbW/R77\n6OhonDlzBh9//DH8/PxgYWGBcePG4dlnn1X7nJcuXcLZs2fx0UcfwcXFBV5eXli2bBnOnj2LS5f+\n9yWNtbU1LC0tYW1tjUGDBiE1NbVR2cPCwtCzZ0/MnTsXFRUVuHXrFt555x3IZDJV0dJQb+GDjw8L\nC6u17dy5c/U+n729PcaPH4/PP/8cCoUCAHDjxg04OjrCxcWlwcc2laOjI+zs7GBlZYVBgwbh3r17\naNOmDUxNTTFo0P0REOnp6QDqf0+rqqpw9epVPPvss5DJZPD09MRTTz2Fmzdvqj3m5mZtbY2pU6di\n+/btUCqVzb5/ItIfLPaoSVpSz55EIoWTuyfO/bEfpcVF+DsjHdfPn4FPUCgAIDPlDgDAxbO16jEe\nvn6qbeq2p99JBAB4BwZrnMnV2wfA/eGhAPD0sFGoqqzEmQP3h0Qd/2U3+g4fff8+aSkAgMm9OmJ4\noBuGB7ohI+k27hUUaP4ikGAuXbqEqqoqFnsNiI6ORu/evQXZ9yeffIKAgABYWVlh5MiRqg/WcXFx\nAIC2bduq7iuRSODj46P2OW/dugUAqiGNABAYGFhr28PEYvFjfai/c+cOAMDDwwMikQgikQgJCQnI\ny8vTyfPNnDkTSUlJqmGcmzZtwowZM3SS5WEPv1Y1w6NrbqvvPZVKpfD29sbPP/+MwsJCpKSk4Nix\nY+jQoUOz5G6s3r17Izk5GTk5OYLsn4j0AydooSZpadfszfx4LT6aMh4vdg2FlY0NOvTsgzFvzq91\nnwc/PCgU938WiUSq2+vbXlVZAaBxE7BUlJUBAEzNLQAAMnsHdO4biSN7dqH3syOQnBCPiaGLAADS\n/1+E+5sLf8HC2kbzgya9cP78eTg6OsLb21voKHqrsLAQcrm82fd79OhRLFiwAN9//z0GDBiA/fv3\nY+LEiQCgWu7BxKTpEyvVbjPu94Dpaq1AU1NTAEBBQQFkMpnOny8kJAR9+/bF+vXrMXDgQFy+fFk1\n5FDbWRqjofcUAL7++msMHjwYjo6OkMvlGDRoEJYsWSJ47rrY2d2fxbmgoADOzs4CpyEiobBnj0gN\npVKJ/du/wrjZb+P7a0n48sw1vLniX6rCycXr/jf1WSnJqsek3U5QbVO33dHFHQCQkZSocaakG/d7\nDdx92qhu6z9yHGLPn8HhH3ei/ZP/W2PK0fX+86feStD4+Ul/nD59Gt26deOC4A1wc3NDUlJSs+83\nLi4Obm5ueOGFFyCTyWpNoFMzYcfly5cb/bwPTvZRIz4+vtY2bfPy8gLwvx7J5ni+mTNn4uDBg1i+\nfDlGjhypsyyN0dB7qlQqsWHDBixduhQVFRXIycnBtm3bVIWdkLnrcvv2bYjFYri6ugodhYgExGKP\nSA2RSAQTMzP8+x/vqoZBjmrbGu+OfRY56anwCQpBQPtw7FizAvl/Z+PvjHRsX70CAe3D4R0YrHZ7\ncEQXOLi4YuuKJart0UcPPpKj9N49VFdXITstBdtXr0C77j3g7vu/D37tuveEk5sHtn68GL2G/m+6\n7Tah7eHu64cvl3+I7LQUtMMlwQAAIABJREFUKKqrkZudhdJ7xc3y+tHjOXPmDLp16yZ0DL3Wt29f\n/P7777h3716z7tfNzQ05OTmIi4tDbm4uLly4oNoWFhaGbt264bXXXsO5c+dQVlamGvoHABYWFjh/\n/jzKy8tR9v899Q8+tmvXrli0aBEyMzORkpKCd999F127dkX79u11ciydOnVCYGAg5syZgzt37qC6\nuhrp6ekoKipSm7cpzwcAQ4YMga+vLz777DOMGzeuUY/VlYbeU5FIBHNzc9WspCKRCGZmZnjqqaeQ\nlJQkaO66/Pjjj+jSpQusrR+9BpyIWg4We0SaUCqx8qdD2B2fjt3x6fjmwg1Y2siwZ9P9RYZnr9qA\n6upqzOjXDW8O7glTc3PMXrVB9fCGtkukJlj4+TbcKyzEjP7d8dHkcbC0kSP/bg5Wz71/DUv3gVHY\nu+3fGNO+DWZH9YGTuzvmfba5VkSRSIS+L4xBaJcnIbN3qHX7O19sg9TEFG8O7okXu4bgy39+gPKy\nUl2/avSYkpOTkZqaiu7duwsdRa+NGzcOVVVVWLVqVbPud+DAgRgwYADCw8PRp08fWFlZoby8HJGR\nkQCAX375BSEhIRgwYADkcjl69uyJK1euAACmTJmCXbt2ISAgAM8//zzmz5+PtLQ0tGvXDllZWdix\nYweqqqrQpk0bBAUFwcLCAjt23F8yZdmyZTh58iQ2bNiAjRs3Yt++fXjvvfeQlJSEyZMnq8393nvv\n4eTJk1i3bh1effX+rMAikQi//vorTE1NERQUBAcHB8yePRslJSWP5B00aNAjj1+6dGmtY8jOzm7w\n+YD718q98cYbmDBhAqysrFS3q8vSkIdzTJ8+HUePHsXGjRvx4YcfYseOHZg3bx6ysrIQHh6u+hsA\nxo8fD39//wbfU6VSicuXL0OpVEKpVKKgoAByuRzLly9/rNzadu3aNXz33XeYPn16s++biPSLSMlp\nmqgJ4uPjERQUhJiYGLRr107oOI2yZMkS/Hvr1/hs33GN7p8QcxHr3p2DNb8erXX7uoWzIRIBry1b\nrYOUTbNn83r4BIUg7KneWn3e1FsJmDmkF65cuVJrwgnSre+++w4TJkxAXl4ev51X49NPP8W7776L\nY8eOoWvXrkLHISN09uxZvPLKK7h+/Xqt219++WWIRCL85z//EShZbSUlJejWrRssLS1x6tQprs9J\n1LLFsQUgUsPU3ALZqcm4+udJVFZUoDD3Lg58tw0nfv0R3QcNFToeDv+4E6XFRbh04ghiL/yp9UKP\nhHPmzBmEhYWx0NPAnDlz0L9/f0RFReHatWtCxxHMnTt3VEMM6/tTM2ukoRH62CwsLHD79m0cPnwY\n5eXlyMnJwRdffIHt27fXuuZQSGVlZXjuueeQnp6O7du3s9AjIs7GSaRO6yeC8NLbH2LT4oXISU+F\nRCKFT3Ao5q/9Nzr0eFroeEiIuYgv3n8LgR0jMOuTdULHIS2qmZyF1BOLxfj+++8xePBg9OzZEz/8\n8AP69OkjdKxm5+3tbbTrqgl9bO3atcOqVaswY8YMJCUlQSqVokOHDti9ezcGDhwoWK4a2dnZeO65\n5xAfH48//vhDo2U+iMj4sdgj0kDk6ImIHD1R/R0FMHXxCkxdvELoGKRlpaWluHLlCubMmSN0FINh\naWmJAwcOYNKkSejfvz8mTZqEVatW1boejOhxTJs2DdOmTRM6xiP27t2LqVOnwszMDKdOnUJQUJDQ\nkYhIT7B/n4hID50/fx4VFRWcnKWRzMzM8PXXX+O7777DDz/8gPbt2+Po0aNCxyLSiby8PEydOhXP\nPPMMunfvjvPnz7PQI6JaWOwREemh06dPw83NDa1btxY6ikEaMWIEYmJiEBAQgL59+2LChAn466+/\nhI5FpBXFxcVYvnw5/P39sX//fhw4cADff/897O3thY5GRHqGxR4RkR46c+YMe/Uek4eHB/bt24cd\nO3YgOjoaISEhmDBhAm7cuCF0NKImKSoqwvLly+Hj44N//vOfmD59Oq5evYoBAwYIHY2I9BSLPSIi\nPaNUKrmYuhaNHDkS165dw9dff43o6GgEBwdj+PDh2L9/PxQKhdDxiNT666+/MG/ePFWRN23aNNy+\nfRv/+Mc/IJPJhI5HRHqMxR4RkZ6Ji4tDTk4OevToIXQUoyEWizFmzBhcu3YN3377Le7evYvBgwej\nTZs2+Oijj5CWliZ0RKJaysrKsH37dvTu3RtBQUH44YcfMGvWLFWRxyGbRKQJFntERHrm+PHjsLa2\nRocOHYSOYnTEYjFGjRqFo0ePIjY2FsOHD8eaNWvg7e2NAQMGYOPGjcjKyhI6JrVQ5eXl+PXXX/HK\nK6/A3d0dL774Iuzt7bFv3z4kJibivffeY5FHRI3CYo+ISM+cOHECTz75JKRSro6jS4GBgfj000+R\nmpqKb775BjKZDHPmzIGbmxt69eqFNWvWICUlReiYZOTu3buHH374AWPHjoWzszOGDh2K2NhYvP32\n20hOTsaPP/6IgQMHcoF0ImoSfpIgItIzJ06cwNSpU4WO0WKYmZlh1KhRGDVqFMrKynDw4EHs2rUL\nH3zwAWbNmgVfX1/069cP/fr1Q//+/WFrayt0ZDJg1dXVuHz5Mg4dOoRDhw7h5MmTqKioQLdu3fDO\nO+/ghRdeQJs2bYSOSURGgsUeEZEeSUxMREpKCq/XE4i5uTmioqIQFRWF8vJyHD16VPWhfPPmzZBI\nJOjSpQv69euHXr16ISIiAtbW1kLHJj1WXV2N69ev4+TJk/jjjz9w5MgR5OXlwdXVFf369cPGjRsx\ncOBAODs7Cx2ViIwQiz0iIj1y/PhxmJmZoXPnzkJHafHMzMwQGRmJyMhIAEBOTg7++OMPHDp0CFu3\nbsXixYshkUgQEhKCbt26oWvXrujSpQsCAwMhEokETk9Cyc7OxtmzZ/Hnn3/izJkzOH/+PIqLi2Fj\nY4NevXrhgw8+QL9+/RASEiJ0VCJqAVjsERHpkRMnTqBLly4wNzcXOgo9xMnJCaNHj8bo0aMBAGlp\naaoP9GfPnsW2bdtQWloKOzs7hIeHo127dqo/wcHBMDMzE/gISJuUSiVu376NK1eu4MqVK7h69Sou\nXryIxMREiEQiBAYGokuXLhg1ahS6deuGkJAQSCQSoWMTUQvDYo+ISI8cP35cVUyQfnN3d8fw4cMx\nfPhwAEBlZSViYmLw559/4tKlSzh27Bg2bNiAsrIySKVSPPHEE2jXrh3at2+PgIAABAQEwM/Pj0Wg\nnlMqlUhOTkZCQgISEhJUhd3Vq1dRWFgIsVgMX19ftG/fHi+99BI6d+6MLl268NpOItILLPaIiPRE\nRkYGbt68yev1DJSJiQk6deqETp06qW6rrq7GjRs3cPXqVVy+fBlXr17F559/juTkZCiVSojFYnh5\necHf3x/+/v6qIrB169bw9vaGpaWlgEfUclRVVSEtLQ1JSUmqou7BP2VlZQAAOzs7hIaGon379pg4\ncSLatWuH0NBQXrdJRHqLxR4RkZ44fvw4pFIpunXrJnQU0hKJRIKgoCAEBQVh5MiRqttLS0sfKSiu\nXr2K3bt311rnz8HBAZ6envD09IS3t7fqZ09PT7i4uMDFxQVWVlZCHJrBqKioQE5ODrKyslQFXUpK\nCpKTk5GSkoKkpCRkZGSguroaAGBlZaUqvocOHaoqwv39/eHo6Cjw0RARNQ6LPSIiPXHkyBF07NgR\nNjY2QkchHbOwsFBdz/ewwsJCJCUlISkpSVWQJCcn49KlS/jpp5+QkZGBqqoq1f0tLS3h7OwMFxcX\nODk5oVWrVqqf7e3tIZfLYWtrW+uPoZ5j5eXlyM/Pf+RPXl4ecnJyVEVdZmam6ufc3Nxaz+Ho6Agv\nLy94enoiPDwcw4YNg6enJ7y8vNC6dWu4uroKdHRERNrHYo9apJJ7xTj92y9CxzAYd7MyhY7QIhw+\nfBgvvPCC0DFIYDKZDG3btkXbtm3r3F5VVYXMzExkZmYiKytLVdTU/JyUlISzZ88iJycHeXl5KC8v\nf+Q5JBIJbG1tIZfLYWdnB1NTU1hZWcHCwgLm5uawtraGiYkJ5HK56r41arY9yMTE5JGhjBUVFbh3\n794j+87Pz4dSqQQAlJSUoLy8HEVFRaiqqkJ+fj6qq6tRUFCAyspKFBcX4969e6qirmY45YNEIhHs\n7Ozg7OwMJycnODs7o23btqqfXV1d4eTkBCcnJ3h6esLCwkL9m0BEZCRY7FGLdDcrEytnTxM6BpFK\nWloaEhIS0KdPH6GjkJ6TSqXw8PCAh4eHRvcvLS1FQUHBI71hNbfl5eWpCqvS0lKUlZUhMzMTlZWV\ntYqvoqIimJiYoKysTFWsPbiPhwsxsVgMuVz+SJ4Hi8Wa4tLGxgZSqRS2trYwMTGBr6+vqgC1srKq\n1Sv5cE+lTCZr4itJRGT8RMqHW2wiDcTHxyMoKAgxMTF1DkMi7Zo4cSIyMzPx+++/Cx2FdGTr1q2Y\nMmUK8vLyOCkH6Z2ysjK4urri3Xffxbx584SOQ0REmokTC52AiNSLiIjAuXPnHvk2nYzHkSNH0L17\ndxZ6pJf27NmD4uJijBs3TugoRETUCCz2iAxA586dUVBQgISEBKGjkI4cPXoUTz/9tNAxiOq0detW\nDBw4kJOXEBEZGBZ7RAYgLCwMZmZmOH/+vNBRSAdu3LiBpKQk9O3bV+goRI9IS0vDoUOH8OKLLwod\nhYiIGonFHpEBMDMzQ9u2bVnsGanDhw/DysoKERERQkchesS2bdsgl8sRFRUldBQiImokFntEBqJz\n5844d+6c0DFIB44cOYKePXvC1NRU6ChEj9i2bRvGjh0LMzMzoaMQEVEjsdgjMhARERG4dOkSKisr\nhY5CWqRUKnHkyBFer0d66cyZM4iPj+cQTiIiA8Vij8hAdO7cGWVlZbh69arQUUiLYmJikJOTw+v1\nSC9t3boVISEh6NSpk9BRiIioCVjsERmIwMBAyGQyDuU0MgcOHICjoyPCwsKEjkJUS1lZGXbu3ImX\nXnpJ6ChERNRELPaIDIRYLEZ4eDgnaTEyBw8eRGRkJMRiNsekX7i2HhGR4eOnCyID0rlzZxZ7RqS0\ntBSnTp1C//79hY5C9AiurUdEZPhY7BEZkIiICFy/fh1FRUVCRyEtOHbsGMrLy1nskd7h2npERMaB\nxR6RAYmIiIBCocClS5eEjkJacPDgQYSGhsLNzU3oKES1cG09IiLjwGKPyIB4eXnB1dWVk7QYid9/\n/x0DBgwQOgbRI7i2HhGRcWCxR/R/7N15XBT14wbwZ1nuU04RkEtRQQXyxAPxQNG8Mk0zzfJK8z5/\nmGmmaYq3pWbZYfnVb2lWKnjfmpUhCoIaCKjcqNw3C/P7w9xv5MHN7M4+79eLFzKzO/MwbrnPfmbm\no2Y6dOjA6/YkIDk5GVFRUTyFk1QO59YjIpIOlj0iNdOhQweexikBJ06cgJ6eHnx9fcWOQlQB59Yj\nIpIOlj0iNePp6YnY2Fjk5eWJHYVq4cSJE+jevTsMDQ3FjkKkxLn1iIikhWWPSM14eXmhvLwckZGR\nYkehGiorK8OJEyfw8ssvix2FqALOrUdEJC0se0RqxtnZGWZmZggPDxc7CtXQ5cuX8ejRIwwaNEjs\nKEQVcG49IiJp0RY7ABFVj0wmQ9u2bVn21FhISAiaNWsGNzc3saMQKT2ZW+/7778XOwoREdURjuwR\nqSEvLy9ERESIHYNqKCQkBEOGDBE7BlEFnFuPiEh6WPaI1JCnpyciIiIgCILYUaia7t+/j8jISAwc\nOFDsKEQVcG49IiLpYdkjUkNeXl7Izc1FfHy82FGomoKDg2FsbIzu3buLHYVIiXPrERFJE8sekRpq\n06YNtLS0eN2eGgoJCUG/fv04ekIqhXPrERFJE8sekRoyMjJC8+bNWfbUTGFhIc6dO8dTOEmlcG49\nIiLpYtkjUlNPrtsj9XH69GkUFhaif//+YkchUuLcekRE0sWyR6SmvLy8OLKnZkJCQtC+fXvY2dmJ\nHYVIiXPrERFJF8sekZry8vJCfHw8cnJyxI5CVXT06FGewkkq5cncerwxCxGRNLHsEakpT09PCIKA\nGzduiB2FqiAiIgL37t3DoEGDxI5CpMS59YiIpI1lj0hNOTo6wtzcnKdyqong4GDY2NigXbt2Ykch\nUuLcekRE0qYtdgAiqhmZTIa2bdvyJi1qIiQkBAMHDoSWFj9jI9XwZG693bt3ix2FiIjqCd91EKkx\n3qRF9ZSUlDy1LCMjA3/88Qev1yOVwrn1iIikj2WPSI15enoiMjIS5eXlYkehv506dQqurq5YuHAh\nLl++jPLychw9ehRaWlrw9/cXOx5poHv37mH37t0oKChQLuPcekREmoFlj0iNtW3bFnl5ebh7967Y\nUehvenp6iI+Px+bNm9GtWzfY2NhgxYoVaNOmDQwNDcWORxooOTkZ48aNg7W1NSZOnIiLFy/ip59+\n4tx6REQagNfsEakxd3d3AMDt27fh6uoqchoCoLzRhUKhAAA8evQIubm5KCkpgbm5Ofr3748hQ4Zg\n2LBhMDExETMqaYgnpxYXFBRg9+7d+Prrr2FhYYHWrVujtLRU5HRERFSfOLJHpMZMTU3RpEkT3L59\nW+wo9Dd9ff2nlj15s52fn49Dhw7h7bffhr29Pe7fv9/Q8UgD/fM60iflLiMjA1FRUXB2doaPjw++\n+OIL5OXliRWRiIjqCcsekZpzd3fHrVu3xI5Bf6vsFvalpaWQyWRYsmQJHB0dGygVabLnjd4pFAoI\ngoDQ0FBMmTIFbm5uyMjIaOB0RERUn1j2iNQcy55qqazs6ejowN/fHwsXLmygRKTpnnWH2H8qKyuD\nTCbD9u3bYWFh0UCpiIioIbDsEam5Vq1aseypkBeVPblcDgsLC+zZswcymawBU5Emq6zsaWlpYdWq\nVRg2bFgDJSIioobCskek5tzd3ZGRkYH09HSxoxCefc3ePx04cABWVlYNlIbocdnT0nr2P/fa2tp4\n9dVXsWjRogZORUREDYFlj0jNPbkjJ0f3VMPzRva0tLQQFBSEbt26NXAi0nQlJSXPHEnW0dGBt7c3\n/vOf/3CkmYhIolj2iNScnZ0dzMzMWPZUxLPKno6ODvr164d58+aJkIg03bNG9uRyOaytrREcHFzp\ndaZERKS+WPaIJIDX7amOf79xlsvlsLS05OgJiebJHWCfkMlk0NHRwZEjR9C4cWMRkxERUX1j2SOS\ngBYtWiAmJkbsGITHp2vK5fIKyw4cOABLS0uREpGme9YNWr7//nt4eXmJkIaIiBoSyx6RBDRv3hx3\n7twROwb9TVdXF8Dj4rd27Vp07dpV5ESkyf55zZ6WlhZWr16NoUOHipyKiIgaAssekQS4ubkhPj7+\nuZMnU8N6UvYCAgIwd+5ckdOQpistLYVCoYC2tjbGjBmDwMBAsSMREVED0RY7ABHVXvPmzaFQKHDv\n3j00b95c7DgV3Lx5E5GRkWLHaHAWFhZ47bXXsH//frGjVMrU1BT9+/cXO4bGKykpQX5+PnJzc1Fc\nXIycnBwAQG5uLhQKRYXHFhQUoLi4uMIymUyGRo0aPbXde/fuoaysDG3atMHixYvx6NEj6OnpwdjY\nuP5+GSIiUgkse0QS4ObmBgC4c+eOypW9H3/8EcuWLRM7higmTJggdoQqcWvREtF/3RY7hiQUFhYi\nOTkZKSkpePjwITIzM5GRkYGMjIxn/vlJacvKyoIgCPWaLTIyUjlVyxPGxsbQ09ODmZkZjIyMYGFh\nAQsLC5ibm1f4bmFhAUtLS9ja2sLW1pbXoBIRqQmWPSIJaNSoEaysrBATE6OSIzRNXZtj85ELYsdo\nMNcunsVLvr3EjlEl+7dtxNXjh8SOoRby8vIQHx+v/EpKSkJKSgqSk5ORmpqKpKQkZGdnV3iOsbHx\nU6XJ2dkZL730EiwsLGBoaAg9PT2Ym5tDV1cXRkZGMDY2hq6urnKUzsDAAPr6+hW2q6enB0NDwwrL\nFAoFcnNzKywrLy/HsmXL8PLLL8PR0VFZLEtKSpCXl4e8vDyUlJQgKysL+fn5yjKanJyMyMjICsX0\nn2VUX18ftra2sLOzQ5MmTZTfnZyc4OLiAhcXF9ja2tbl4Sciohpg2SOSCN6kRXWoS9Gjp2VlZeHW\nrVuIiopCbGws4uLicPfuXcTFxeHhw4fKxzVu3Bj29vaws7NDs2bN4OvrW6H02NnZwcrKSnn9ZkPQ\n1taGubn5U8vXrFlTJ6dsPnz4EGlpacqRyydFNyUlBWFhYUhOTkZiYqLy2mEDAwO4uLjA1dVVWQA9\nPDzg7u4OR0fHWuchIqLKsewRSYSbmxvLHlEV5efn4/r164iKisLNmzeVX0lJSQAAIyMjNG/eHC4u\nLujWrRvGjh2rLCwuLi5Pjaqpsrq6Ns/KygpWVlZo3br1cx9TVlaGxMTECiOg8fHxCAsLw759+5CW\nlgYAMDExgbu7O1q3bq387u3tDTs7uzrJSkREj7HsEUlE8+bN8fvvv4sdg0jllJaWIjo6GlevXlV+\n/fnnnygpKYGenh6aNWuG1q1bY9KkSWjdurVy9ElLizesri65XA4nJyc4OTmhZ8+eT63PyspCbGys\nsmRHRUXh/PnziI+PhyAIaNKkCdq3b6/86tixI08HJSKqBZY9Ion45/QLOjo6YschEs2DBw9w8eJF\nnD9/HufPn0dUVBQUCgXMzc3Rvn17+Pr6Yu7cuWjXrh1cXFzEjqtRGjVqpCxy/5SVlYWwsDCEhobi\n6tWr2L17N5YvXw4AcHBwgK+vL3r06AE/P7+nbjJDRETPx7JHJBHNmjWDQqFAQkICXF1dxY5D1GCy\nsrJw8uRJnD9/HufOncPNmzehpaUFT09P9OrVC4sXL0b79u3RrFkzsaPSczRq1Ai9e/dG7969lcsy\nMjJw9epVXLlyBRcvXsTChQuRl5eHxo0bK4tf37590aJFCxGTExGpNpY9IolwdnYG8HhOLZY9krq4\nuDgcPnwYwcHBuHDhAsrKytCyZUt0794dy5YtQ58+fWBhYSF2TKoFCwsL9O3bF3379gXw+HrA69ev\n49KlS/j111+xdOlSzJgxA66urvD398egQYMQEBDQoDfFISJSdSx7RBJhY2MDIyMj3L17V+woRPUi\nLCwMe/bswaFDh3Dnzh1YWlqif//++PbbbxEQEPDMO1GSdMjlcuUpoLNnz4ZCocCvv/6KkJAQBAcH\n44svvoCZmRkCAgIwevRoDBgwAHp6emLHJiISFcsekYQ4Ojri3r17YscgqjMJCQnYs2cPdu/ejZs3\nb8LNzQ0jRozAwIED0aVLF8jlcrEjkki0tbXh5+cHPz8/rF27FnFxcQgODsbBgwcxfPhwNGrUCCNH\njsTYsWPRtWtXyGQysSMTETU43mqMSEKcnJw4skdqr7y8HAcPHoS/vz+cnZ2xfv169OrVC5cvX0Z0\ndDRWr16N7t27s+hRBa6urpg1axZOnz6N+/fvY9GiRfj111/RvXt3uLm5Yd26dU9Nek9EJHUse0QS\n4uzszLJHais/Px/btm1Dq1at8Oqrr0JfXx8///wzkpOTsXXrVnTp0kXsiKQm7O3tsXDhQkREROD6\n9esYPHgwVq5ciaZNm2L27NmIi4sTOyIRUYNg2SOSEI7skToqLCzEqlWr0LRpUyxcuBC9e/fGzZs3\nERwcjCFDhvCGG1QrXl5e2LRpExISErB8+XIcOnQILVq0wOuvv87SR0SSx7JHJCHOzs5ISkqCQqEQ\nOwpRpQRBwN69e9GqVSusWbMGs2fPxv3797Fjxw60bNlS7HgkMaamppg7dy5iYmKwd+9ehIeHw8PD\nAwsXLkRWVpbY8YiI6gXLHpGEODk5QaFQIDExUewoRC908+ZN+Pj44M0330Tfvn0RHR2NZcuWwcrK\nSuxoJHHa2toYOXIkIiIisGHDBuzatQtubm747rvvxI5GRFTnWPaIJOTJXHs8lZNU2ddff42OHTtC\nS0sLYWFh+PLLL9GkSROxY5GG0dHRwfTp0xETE4M33ngDb7/9NsaNG4e8vDyxoxER1RmWPSIJsbW1\nhb6+PsseqaSCggKMGTMGkydPxsyZM3Hx4kV4eXmJHYs0XKNGjbBlyxaEhITg2LFj6NChAyIjI8WO\nRURUJ1j2iCREJpPBwcGBp3HWws87t2Fs+xaY2K3uSsilkF/wbp/OeN3TGdcunn3mst3rV2Lpm6/W\n2T5VTW5uLgYMGIDjx4/j6NGjWLNmDbS1NW+q16CgIJiamsLW1la5LDAwEH5+flV+fHWMHDkScrkc\nCxYsqNHzG2pfy5Ytg7GxsfLsBDEMGDAA169fh42NDfz8/PDnn3+KloWIqK6w7BFJjL29PZKTk8WO\nobaGTZ6Otxd9WGfby8vJxtbFczFn/Tb839avUVSQ/8xlUlZWVoZRo0YhOjoa58+fR79+/UTNc+rU\nKWzduvW5P9enwMBAbNy4sd4eD1T8ffbt2wdfX99qPf9526pMbfa1fPlyrFmzpkbPrUt2dnY4fvw4\nOnfujAEDBuDOnTtiRyIiqhXN+1iVSOLs7OyQlJQkdgy1JpPJ6mxbKXfjUFpcDIdmLWBkagoAiIm4\n9tSyLgGD6myfqmblypU4d+4cLly4gNatW4sdBzt37qxQSv79c3379+srKCioWo+vTF3+Pg19bFSB\ngYEBfvzxR/j5+WHEiBH4888/oaOjI3YsIqIa4cgekcTY29urddn7/tP1eK11U0zs7o3JPV7CqLZO\nGO3tioWvBmB6v64Y274FRnu7YscHC5XPeZCciNVT38KYdm4Y37Ut1s2ajMwHaVVen/XoAdZMextv\neDfD614u2LNpdbUyH/pmByb3eAkj2zhhSq+OuHzsMADg8rHD+HjqOADAuE6tMLyVHS6F/PLUsl+P\nHMKbHVthsl87AMDeTWswwsMBezauxoHPP8G7fTojaPr4ah8XVXDnzh2sWrUKq1evRocOHep9fxMm\nTICWlhbWr18PANjuYv3nAAAgAElEQVS8eTOMjIyU+544cSL279+PmTNnQiaTYeDAgRV+Tk1NRVxc\nHAICAmBmZgYXFxd89dVXAID3338fcrkcixcvxscffwwXFxeEh4dXmiktLQ1Dhw6FkZERDAwMsHjx\nYuW6AwcOoFGjRnBwcKjS4yvz798vNTUVABAXF4cxY8bA0tISdnZ2OHLkiPI5GzZsgL29PXR1deHk\n5IT9+/e/cFuVedG+nnds/+3DDz+ElpYWevfujWbNmsHIyAg9e/ZssJE2Q0NDfP/994iJicEnn3zS\nIPskIqoPLHtEEqPuZe/1mQvQulMXeHbpjk3B5/DdlVvw7t4TBXm5WPrlXuz6PQozPt6Mk/v2IOVe\nPABgw9ypkGnJsO3Er1h34Bjyc7MRNGOicpuVrf94yjjkZmViy5Hz2BMWg2GTZ1Qr86PUFCzZuRd7\nrkaja//B+G7tRwCArv0H4/8+ffxm9rsrt3HgdjK6D3zlqWXdXh6CsfP/94b+jbmL4NG+M2JuXIN7\nu04YMHY8Rs6o/nFRBRs3boSTkxNmzKjeMa2pr7/+Gi1atFD+PGfOHLz33nvKn7/66is4Ojri008/\nhSAICAkJqfCztbU1+vXrh+bNmyMhIQGff/45Jk+ejCtXrmDVqlXw9fXFlStX0L17d8yYMQOCIFSa\nadCgQXj06BFu3ryJ3NxcLFq0SLlu+PDhT52++KLHV+bfv9+Ta/1sbGywc+dOJCQkoEuXLli2bJny\nOYmJiTh69ChycnLw2muvYeHChS/cVmWet6+ysrLnHtt/+/DDD+Hp6QkPDw9EREQgJiYG2traePXV\nhruutVmzZpg5cybWr1+P0tLSBtsvEVFdYtkjkhh7e3ukp6er/cTqJuYWMDY1g56BIdr16I2iggLY\nOjpDW0cH7Xr0AgBkpKci/mYkYsLDMHpOIBpZ2cCqiT3GzH0PMeFhiL8ZWen62MhwxEaGY9zCpbC2\nc4Bcrg0DI+NqZR3/3nI4tXSHjp4eWni3Q9ajB3VyDFxatYFHRx8MGT8VLu6tq3VcVIEgCPjll18w\nYcIEyOVyseNUyZUrVxAbG4vFixfD1NRUWU5OnTqlfIy3tzd69OiB+fPnw9vb+4XbCw0NRWhoKNau\nXQsnJydoa2vDxMSkzh5fVcbGxjA0NIShoSECAgIq3MRp06ZN8PT0hL6+Pnx8fJCWlvaCLdV8X1U5\ntv9ma2sLIyMj2NnZ4aOPPsKNGzdw7dq1WuWrjsmTJyM1NRWXL19usH0SEdUlXrNHJDH29vYoLy9H\nampqhVPD1JlMpgX8YwRFpvX351SCgNSEuwAA26ZOyvUOrs0BQLnuResVf39i79zKo8b5Dn61HSf3\n7UFGWipKioug3UDX97zouKiCBw8eICUlBT4+PmJHqbIn05b8+7+dzMzMGm0vOjoaAKo8xUR1H18T\nWlpaFUYk161bh507dyIpKQmFhYXQ1dWtl33V9ti2bNkSABAfH4+XXnqpzjK+SLNmzdC4cWOEh4c/\n946pRESqjGWPSGLs7OwAAElJSZIpe1Xxzzev5eWP/yyTyZTLn7deUVoCAJBr16ygRV25jN3rV2H+\n5s/h3c0P1y6exSeBs2q0LanJzc0FAJj+fRMadfCk6GRnZ9dJ7pKSx6+vqt7go7qPr61z584hMDAQ\n+/btQ79+/XDs2DGMGzeuXvZV22P75FTKhp6yw9TUVPlaJiJSNzyNk0hi7OzsIJPJ1Pq6veqwdXQB\nAKQl3FcuS4qPUa6rbL2VrT0AIOVeXI32nxgbA3ObxugSMAgGxib/G10j2NjYQCaTNfi8jzKZDOXl\n5TV6rqOjIwDg1q1bdZKladOmAICYmJh6eXxt3bp1C3Z2dhgxYgRMTU2hVY+v39oe2ycTnbu5udVZ\npsooFAqkpKTUeJ5DIiKx8V0JkcTo6urCyspKY8qei3trtPBqj/9uCULWw3Q8TEnG3k1BaOHVHs6t\nPCpd79GxMyxtm+DboBXK9VfPnazy/s1tbJGT8QiJsTHIy85CbGTld2fUFCYmJvD29saxY8cadL9N\nmjTB6dOnkZeXh6ysLKSkpFRYb2BggD///BPFxcUoKiqq8HObNm3QqlUrzJs3D3fv3kVZWRmSk5Nr\nPLLTo0cPODg4YMGCBUhNTUVCQgKCg4Pr7PHP8u/f70Xs7Ozw4MED3Lp1CxkZGQgNDa3xtirToUOH\nah/b/Px8lJaW4v79+1i6dCn8/Pzg7u5eqxzVcfHiReTl5Wnc9BNEJCECUQ3cunVLACCEh4eLHYWe\nwdvbW1i0aJHYMQRBEITly5cLTV2bCwduJ1fpa+SM+YKWXC7o6OkJI2fMF+Zu2C4YmpgKAARXj7bC\nV7+GC3bOrgIAwaKxrbDtxGXhs9N/CO38+gh6+gaCnoGh0L6nv/DZ6T+U26xs/fqfTwotvNoLegaG\nQtPmLYWer4wUAAjdB75Sad7vI+4KHXr2FXT19QXnVh7C67MWCgAEr25+QtD+I4KlbRMBgGDVxF6Y\ntHTVM5e9MSdQ0Dc0EgAITi3cBf/X3nh8DHR1hd7DX6/xcanK8X595gLBrUWLevv737Rpk2BkZCSk\npaXV2z7+7dixY0Ljxo0FU1NTYejQocLo0aMFLS0tYebMmYIgCMLGjRsFAwMDwdHRUbh06dJTP9+5\nc0fo2bOnoK+vL5iZmQkjR44UUlNThffff1+Qy+WCnp6eMGHChCrnuXbtmuDj4yMYGRkJrVu3Ft56\n6y0BgDB69Ghh5cqVgrGxsQBAaNu2rZCamvrCx1fFP3+fnj17CnK5XDAwMBB27NghnDx5UjA3NxcA\nCGPHjhWKioqEQYMGCQYGBoKXl5ewYsUKAYDQr1+/Zx6rF3lyfJ63L0EQnntsly9frjwOnp6eQnp6\nuuDl5SWYm5sLurq6goGBgfDKK68IDx48qPJxrwsvv/yy0LVr1wbdJxFRHbopEwQVuZKf1Mrt27fh\n7u6O8PBweHp6ih2H/mXAgAFo0qQJvv76a7GjYMWKFfjy293YfOSC2FHoGfZv24irxw8h+q+/6mX7\n+fn5aN26NTp16oR9+/bVyz5Imry9vTFy5MhqzTNYl3788UeMHDkSp06dQu/evUXJQERUS7d4GieR\nBFlbW+PBg7q5/b8mS09KwPBWdi/8Sk9KEDumSjMyMsJXX32Fn376CWvXrhU7Tp26e/cuZDLZC7+e\n3IFSHfenCvuu6bWXtRUeHo4JEyZgypQpLHpEpNZ4N04iCbK2tlbewp1qzsa+KQ7cThY7htrr06cP\nNm7ciDlz5kBbWxvz5s0TO1KdcHZ2rtKk6uq6PzH3PX36dERERCA6OhqpqanYunVrg+372rVrCAgI\nQKdOnbBly5YG2y8RUX1g2SOSII7skaqZNWsWysvLMW/ePERGRmLr1q0wNDQUOxapqG3btmHbtm0N\nvt/vvvsO06dPR5cuXfDzzz/X6ZyDRERi4GmcRBLEskeqaM6cOTh48CAOHTqE9u3bIyIiQuxIRACA\nwsJCzJ49G2+//TYmTJiA4OBgGBkZiR2LiKjWWPaIJMja2hq5ubm1vlU6UV0bPHgwrl27BisrK/j4\n+GDJkiXIy8sTOxZpsAMHDqBt27bYu3cvDh8+jC1btnBEj4gkg2WPSIKsra0BAA8fPhQ5CdHTmjZt\nirNnz+Kjjz7Ctm3b4Obmhi+//BJlZWViRyMNcuXKFfj6+uK1116Dj48PwsPDMXDgQLFjERHVKZY9\nIgl6UvZ4KiepKm1tbcyfPx937tzBiBEjMG3aNHh6euKrr75CcXGx2PFIwi5duoRhw4bBx8cHMpkM\nf/zxB/7zn//Azs5O7GhERHWOZY9Iglj2SF1YWlri008/xY0bN9CxY0dMmzYNTk5OWLFiBV+/VGcU\nCgV++OEHdOrUCb6+vkhLS8PPP/+MCxcuoGPHjmLHIyKqNyx7RBJkZmYGXV1dvlkmtdGyZUvs2rUL\nd+/exaRJk/DJJ5/A0dERb7zxBo4cOQKFQiF2RFJDUVFReO+99+Di4oIxY8bA2dkZv/32Gy5fvoyh\nQ4eKHY+IqN6x7BFJlJWVFcseqZ0mTZpg5cqVuH//Pj799FMkJCRg0KBBsLe3x+zZsxEaGip2RFJx\nKSkp2LRpE9q1a4c2bdrgv//9L8aNG4c7d+5g37598PHxETsiEVGD4Tx7RBJlaWmJjIwMsWMQ1Yih\noSEmTZqESZMmISEhAXv37sU333yDTz75BE5OTggICIC/vz9efvll3iKfEBcXh8OHDyM4OBjnzp2D\nkZERhgwZgrVr16JPnz6QyWRiRyQiEgXLHpFEmZmZITs7W+wYRLXWtGlTBAYGIjAwEKGhoTh48CBC\nQkLwxRdfwMjICH379sXAgQPRq1cvNGvWTOy41ACys7Nx8eJFHDlyBCEhIbh//z5sbW0xcOBA7N+/\nH/3794e+vr7YMYmIRMeyRyRRLHskRR06dECHDh3w0UcfISEhASEhIQgODsasWbNQWFgIBwcH+Pn5\nwc/PDz169EDLli3Fjkx14NGjR7h48SLOnz+P8+fPIyIiAuXl5fD29sZbb72FQYMGoUOHDtDS4tUp\nRET/xLJHJFEseyR1TZs2xdSpUzF16lQUFxfjypUryjIwd+5c5Ofnw9bWFj4+PujQoQPat2+PDh06\nwMrKSuzo9ALFxcUIDw9HaGgoQkND8eeffyIqKgoymQxt27aFn58flixZAl9fX+Wdh4mI6NlY9ogk\nyszMDCkpKWLHIGoQenp68PX1ha+vL5YsWYLS0lKEhobiwoULuHLlCnbu3IklS5YAAJydnZXFr02b\nNvDw8ICzszNHhUTw6NEjREVF4ebNmwgLC0NoaCgiIyNRWloKMzMztGvXDgMGDMCqVavg6+sLc3Nz\nsSMTEakVlj0iieLIHmkyHR0ddOnSBV26dFEue/DgAUJDQ3H16lWEhoZi+/btSEhIAPD4hjCtWrWC\nu7s7WrduDXd3d7Ro0QIuLi4wMDAQ69eQhLKyMiQmJiI2Nha3bt1CVFQUbt++jcjISOUdg01NTeHt\n7Y2ePXtiwYIF6NChA9zc3HhjFSKiWmLZI5Iolj2iiqytrTFgwAAMGDBAuSw7O1tZPG7fvo2oqCh8\n8cUXuHfvHgRBAPB4OggXFxe4uLjA1dVV+d3Ozg729vYwNDQU61dSCaWlpUhPT0dSUhLu3r2L+Ph4\nxMfHIy4uDvHx8bh37x5KS0sBABYWFnB3d4eHhweGDBkCDw8PuLu7o2nTpiL/FkRE0sSyRyRRpqam\nLHtElTAzM0Pnzp3RuXPnCsvz8/MRGxtbobTExcXhwIEDiI2NRXFxsfKxJiYmsLe3R+PGjeHg4KD8\nbmlpCQsLC5ibm8PCwkL5paOj09C/ZrVlZWXh0aNHyMjIQEZGBjIzM/Hw4UOkp6cjMTERaWlpSEpK\nQlpaGtLS0pTFWC6Xw97eXlmKe/TooSzKzZo1Q+PGjUX+zYiINAvLHpFEqdLIXkF+Hi4fPSR2DHqG\n+3eixY6gkoyMjODp6QlPT0/lsrKyMmzatAkffPABWrdujWXLlsHAwAApKSlITk5GamoqkpOTERMT\ng6SkJGRkZKCwsPCpbZuYmChLoIGBAYyMjGBsbAxdXV00atQI+vr6MDAwgKmpKeRyOYDH/z3/85pC\nmUyGRo0aVdhuQUFBhRIKALm5uVAoFACAnJwclJSUICcnR/nYrKwslJSUIC8vDzk5OcpiV15eXmE7\ncrkclpaWsLGxgb29PWxtbeHl5QVbW1vY2dkpvzs4OEBXV7d2B5+IiOoMyx6RRJmZmaG0tBRFRUWi\nzzf1KC0VG+ZOFTUDPV8LTk9QqaioKEycOBFhYWGYN28eVqxYUaVSU1hYWGF07Mmfn/xcVFSEvLw8\n5OXlobi4GHFxcSgsLERRURGys7NRXl6O8vLyCh/clJeXo6io6Klip6en99QppYaGhtDT0wPwuGTq\n6urCzMwMBgYG0NfXh7OzM/T09GBkZARTU9NnjkRaWFjAzMysDo4iERE1NJY9Iol68uYsOztb1LL3\nwQcf4IMPPqjy46Ojo/HKK68gOzsbP/3001On1xE1JIVCgQ0bNmDZsmXw8PDAlStX4O3tXeXnGxgY\nwN7eHvb29nWW6fDhwxgyZAhyc3NhbGxcZ9slIiLp4X2miSTqSdnLysoSOUnVHTlyBJ07d0ajRo0Q\nGhrKokeiioiIgI+PD5YvX47ly5fjzz//rFbRqy+JiYkwNzdn0SMiokqx7BFJ1JPTuQoKCkROUjlB\nEBAUFITBgwdj0KBBOH36NJo0aSJ2LNJQpaWlCAoKQseOHaGvr49r164hMDBQef2c2BITE+Hg4CB2\nDCIiUgM8jZNIop6cullUVCRykhfLy8vD+PHj8csvv+Djjz9GYGCg2JFIg/3222+YOHEi7t27hxUr\nVmDhwoUqN9l6YmIipyogIqIqYdkjkih1KHsJCQkYNmwY7t69i+PHj6N3795iRyINVVhYiOXLl2P9\n+vXw9/fH0aNH4eTkJHasZ0pISICbm5vYMYiISA2o1seVRFRnVL3snTlzBi+99BLKy8tx9epVFj0S\nzaVLl+Dt7Y0dO3Zg+/btKl30AJ7GSUREVceyRyRRqlz2vv32W/Tv3x/+/v64dOmSSr+xJunKycnB\n7Nmz4efnBzc3N0RGRuKdd96BTCYTO9oLJSUlsewREVGVsOwRSZS2tja0tbVVruwFBQVh/PjxePfd\nd7F3796n5gUjagjHjh1D27ZtsXv3bnz22WcIDg5WiwL16NEjFBQU8Jo9IiKqEpY9IgnT19dXmbKn\nUCgwdepUvP/++9i6dSu2bNmicje+IOnLysrClClTMGDAAHTu3Bl//fUX3nnnHbFjVVlCQgIAqEUx\nJSIi8fEGLUQSpq+vj8LCQrFjID8/H8OHD8evv/6KgwcPYuDAgWJHIg0UHByMqVOnoqysDAcOHMCr\nr74qdqRqS0xMBIA6naSdiIikix+rE0mYKozsZWRkoG/fvggLC8O5c+dY9KjBpaenY9y4cRg8eDC6\ndu2KyMhItSx6wOORvUaNGsHExETsKEREpAY4skckYWKXvdTUVPTv3x8PHjzAmTNn0KZNG9GykGba\nv38/pk2bBiMjIxw/fhz9+vUTO1KtJCUl8Xo9IiKqMo7sEUmYvr4+iouLRdl3fHw8fH19UVxcjN9+\n+41FjxpUSkoKhg0bhlGjRuHVV1/FjRs31L7oAY9H9ni9HhERVRXLHpGE6enpiTKyd+vWLXTr1g1m\nZma4cOECHB0dGzwDaSZBEPDdd9+hTZs2iIiIwOnTp/H5559L5rTHxMREjuwREVGVsewRSZhMJoMg\nCA26z6ioKPTq1QsuLi44c+YMrK2tG3T/pLnu3r2Lfv36Yfz48RgxYgQiIiLQq1cvsWPVqYSEBN6c\nhYiIqoxlj0jCGnpy6Fu3bsHf3x9ubm44duwYTE1NG3T/pJkEQcAXX3yBtm3bIiUlBb/99hs+//xz\nGBkZiR2tzvGaPSIiqg6WPSKJa6iRvevXr6NHjx5wc3PDkSNHJHPaHKm22NhY9O7dG9OnT8f06dNx\n9epVdOrUSexY9eLJhOq8Zo+IiKqKZY9IwhrqNM4bN26gT58+8PLywrFjx1j0qN4pFAps2bIFnp6e\nyMjIwO+//441a9ZAT09P7Gj15skceyx7RERUVZx6gUjCGqLsRUdHo2/fvmjbti0OHz4MAwODet0f\nUWRkJCZMmIAbN24gMDAQixcvhq6urtix6l1CQgIAlj0iIqo6juwRSVh9l72EhAQEBATAycmJRY/q\nXWlpKYKCgtC+fXtoa2sjLCwMH374oUYUPeDxyB4nVCciourgyB6RhNXnDVrS09PRt29fmJiY4OjR\no3wDSvXq+vXrmDBhAm7fvo0VK1ZgwYIFkMvlYsdqUImJiRzVIyKiauHIHpHE1cfIXlZWFvr06QMA\nOHnyJCwsLOp8H0QAUFRUhEWLFqFDhw4wNjbG9evXERgYqHFFD+Ace0REVH0c2SOSsPo4jbO4uBjD\nhg1DZmYmLl++jMaNG9fp9omeuHz5MiZOnIjk5GRs2LABM2fOhJaW5n5GmZCQgGbNmokdg4iI1Ijm\n/qtJpAHquuwJgoDJkycjLCwMwcHBcHR0rLNtEz1RUFCARYsWwdfXF87Ozrhx4wZmz56t0UUP4Gmc\nRERUfRzZI5Kwur5mb+HChfj+++8REhICb2/vOt02EQBcuHABkyZNwoMHD/DZZ5/hnXfeETuSymDZ\nIyKi6tLsj0mJJK68vLzOCt/WrVuxceNG7Nq1C3379q2TbRI9kZ2djSlTpqBnz55o2bIlIiMjWfT+\nISMjAwUFBbxmj4iIqoUje0QSVlRUVCfTIZw6dQpz587FqlWr8MYbb9RBMqL/OXLkCKZOnYri4mLs\n2rUL48aNEzuSyuEce0REVBMc2SOSsKKiIujp6dVqG3fv3sXo0aPxyiuvYNGiRXWUjAjIzMzElClT\nMHDgQPj4+CAqKopF7zkSExMBsOwREVH1cGSPSMKKi4trVfby8vIwePBgNG3aFN9++229zttHmmX/\n/v2YMWMG5HI5fv75Z7zyyitiR1JpCQkJMDMz43yWRERULRzZI5KwoqIi6Ovr1+i5giBgwoQJePDg\nAQ4ePAhDQ8M6TkeaKC0tDSNGjMCoUaMQEBCAyMhIFr0qSEpK4vV6RERUbRzZI5Kw4uLiGpe91atX\n4+DBgzhz5gzfZFKd2L9/P959912YmJjgxIkT8Pf3FzuS2khISOApnEREVG0c2SOSsJqO7F24cAHL\nli3DunXr0K1bt3pIRpokOTkZQ4cOxahRozB8+HDcuHGDRa+aEhMT+aELERFVG0f2iCSsJjdoefDg\nAd544w30798fM2fOrKdkpAkEQcDOnTuxYMEC2NjY4MyZM+jZs6fYsVTeRx99hPv378PBwQGOjo6w\nt7dHbGwsOnfuLHY0IiJSMyx7RBJVVlYGhUJRrZG98vJyvPnmm5DL5bwhC9VKXFwcJk+ejAsXLmDa\ntGlYvXo1r/usovLycnz55ZfQ0dFBWVkZysvLAQAff/wxtmzZAltbWzRr1gxOTk5477334OLiInJi\nIiJSVSx7RBJVXFwMANUqe0FBQTh79iwuXrwICwuL+opGakoQBCgUCujo6Dz3MU+Kyvz58+Hs7IzL\nly+jY8eODZhS/T05dbq0tPSpdfn5+YiNjUVsbCycnJzw2WefNXQ8IiJSI7xmj0iiioqKAFS97F2/\nfh3Lli3D6tWr0alTp/qMRmpq48aNWL58+XPX37lzB7169cKMGTMwffp0XL16lUWvBnx8fCCXy1/4\nGC0tLSxZsqTSxxERkWZj2SOSqCcje1W5Zq+kpARvvfUWOnXqhDlz5tR3NFJDly5dQmBgINasWYNr\n165VWKdQKBAUFIQ2bdogJycHf/zxB9asWQNdXV2R0qo3Y2NjtGrV6oWPsba25gT0RERUKZY9IonK\nzs4GAJiamlb62A8//BCxsbHYtWsXtLT4vwWqKD09HcOHDwcAyGQyjBs3TnmKYUREBLp06YIPP/wQ\ny5cvR2hoKF566SUx40pCr169nluW5XI5Fi9ezDJNRESV4rs6IonKzMwEAJibm7/wcWFhYVi/fj3W\nr1+P5s2bN0Q0UiPl5eV4/fXXkZmZqbzpz61bt7B69WoEBQWhY8eO0NXVxbVr1xAYGMjTCutIt27d\noFAonrnO2NgYEydObOBERESkjniDFiKJysrKAvDisldSUoKxY8fCz88PU6ZMaahopEaWLl2KCxcu\noKysTLmsrKwMK1asgJ6eHjZs2IBp06ZxRLiOde3aVXkXzn/S1tZGYGAgjIyMREhFRETqhmWPSKIy\nMzOho6PzwjeF69evx71793D06FFOs0BPCQkJwerVqyEIwlPrtLS00KxZM7z77rssevXA0dERjRs3\nRlpaWoXlenp6ePfdd0VKRURE6ob/QhNJVGZm5gtH9e7fv4+PP/4YS5YsgZOTUwMmI3Vw//59jBkz\n5rkfApSWluLmzZvYsmVLAyfTHH5+fhVOi9XR0cHcuXPRqFEjEVMREZE6YdkjkqjKyt7s2bNhZ2eH\nefPmNWAqUgfFxcUYOnQoCgoKnnkq4RNlZWVYvHgx7ty504DpNEf37t0rlG25XI5Zs2aJmIiIiNQN\nyx6RRGVmZj53BOD48eP45Zdf8Mknn1RpagbSLHPmzEFkZOQzJ/X+t+LiYkyaNOmZp3pS7fzzJi06\nOjqYNm0arK2tRU5FRETqhNfsEUnU80b2SkpKMGPGDIwaNQr9+/cXIRmpsj179mDHjh3PXS+XyyEI\nAsrLy2FnZwd/f3/07NkTRUVFMDAwaMCk0ufp6QkDAwMUFhYCAEfhiYio2lj2iCQqKyvrmWVv586d\nSExMxOnTp0VIRarsr7/+wuTJkyGTyZQjdbq6ulAoFCgvL4eVlRV69uyJvn37wt/fH66uriInljZt\nbW106tQJ58+fx/jx42Fvby92JCIiUjMse0QSlZmZCQ8PjwrL8vPzsXLlSkybNg2Ojo4iJau948eP\nKyeNp7pRWFiIwMBA5SgSAFhZWcHT0xMeHh7w8PCApaWlcl1oaCiio6NVYnQ4NzcXR48eFTtGvbC2\ntoaWlhbatGmDffv2iR2nVgICAmBmZiZ2DCIijcKyRyRRzzqNc/PmzcjLy0NgYKBIqerG3HnzcOvm\nTbFjSN7Dhw9x5swZnDlz5pnr3Vq0RPRftxs41dMSExMxatQosWPUKyncmCUiIgJt27YVOwYRkUZh\n2SOSqH/foCUzMxMbNmzAggULYGNjI2KyuvHatLl4fdZCsWNIQklREQryctDIquqvi/3bNuLq8UP1\nmKr6Nh46DacW7mLHqFMFuTnISEuFQ/MWYkepscTYGMwe6Cd2DCIijcSyRyRB5eXlSElJqXCNz7p1\n6yCXy3mTB3qKrr4+dPX1xY5Bz2BoYgpDE1OxYxARkZri1AtEEpSeno7S0lJl2cvJycH27dsxb948\nmJiYiJyOiHjWl+wAACAASURBVIiIiBoCyx6RBCUmJgKAsux98cUXKC8vx7vvvitmLCIiIiJqQCx7\nRBKUlJQEmUwGe3t7lJaW4tNPP8WkSZOeO8k6EREREUkPyx6RBCUlJcHS0hL6+vr4/vvvkZycjNmz\nZ4sdi4iIiIgaEMsekQQlJSUpT+HcsGEDRo4cCScnJ5FTEREREVFD4t04iSQoMTER9vb2+OOPPxAe\nHo4vvvhC7EhERERE1MA4skckQUlJSXBwcMCuXbvg4eGBTp06iR2JiIiIiBoYyx6RBCUmJqJx48b4\n4YcfMH78eLHjEBEREZEIWPaIJCg5ORlpaWnIzc3FmDFjxI5DRERERCJg2SOSmJycHOTm5iI0NBT9\n+/dHkyZNxI5ERERERCJg2SOSmLi4OABAeHg4xo0bJ3IaIiIiIhILyx6RxPz111+Qy+XQ1tZG//79\nxY5DRERERCLh1AtEEvPXX3/BwMAA3bp1g4mJidhxiIiIiEgkHNkjkphbt26hqKgIgwcPFjsKERER\nEYmIZY9IYsLCwqBQKDBw4ECxo9Dfft65DWPbt8DEbl6iPJ9US338fV4K+QXv9umM1z2dce3i2Wcu\n271+JZa++Wqd7ZOIiFQfT+Mkkpi7d+/C3t4ezs7OYkehvw2bPB0mjczx381BojyfVEtd/33m5WRj\n6+K5WL5rP/Jzc1BUkP/MZUREpHlY9ogkJCUlBSUlJfDx8RE7Cv2LTCYT9fmkWury7zPlbhxKi4vh\n0KwFjExNAQAxEdeeWtYlYFCd7ZOIiNQDyx6RhNy4cQMA0Lt3b5GTqJbvP12PAzu2wNTcElpaMuRk\nZkBLLoeDqxsK8nKR/eghysoU8BsyHFNXrAMAPEhOxJcr3kfklcvQ1deHRwcfTFq6EubWjau0PuvR\nA+xYuhARly+iXCiHobEJZKj6G/zKnp+WcA+ff7gI0eFhMDFrhBHvzkGfEaNxYMcWfP/penTtPwha\nWnKEnT8NXQMDzFy9BZ5dfbFh7lRcPnoIevoGeH32QgwZPxXbl8zHuV9+hHMrD6z98WgdHnnVpI6v\nh0Pf7MDhbz5HdkYGzK1t8FbgB+jafzAuHzuMnSsWAwDGdWoFAJi7YTu+WrW0wrJ5G3dgx7L/g76h\nIXaeD8PeTWvw086tGDZpOvSNjHBq3x44t/JA6LlT1TouRESk2njNHpGEnDp1CgA45cK/vD5zAVp3\n6gLPLt2xKfgcvrtyC97de6IgLxdLv9yLXb9HYcbHm3Fy3x6k3IsHAGyYOxUyLRm2nfgV6w4cQ35u\nNoJmTFRus7L1H08Zh9ysTGw5ch57wmIwbPKMamV+0fPLy8qwYuJoNHFywc5zoZi6Yi0+W7oAMRHX\nMHzqbLi36wRDY1NM/WgdPj8XCqcW7ti3bQMAYP6mHbBzdkWX/oMwZPxUAMC0lRvQwrsdVu35pcbH\nWJ2o4+vhUWoKluzciz1Xo9G1/2B8t/YjAEDX/oPxf59+BQD47sptHLidjO4DX3lqWbeXh2Ds/MXK\n7b0xdxE82ndGzI1rcG/XCQPGjsfIGdU/LkREpNpY9ogk5I8//oC2tjZcXV3FjqKSTMwtYGxqBj0D\nQ7Tr0RtFBQWwdXSGto4O2vXoBQDISE9F/M1IxISHYfScQDSysoFVE3uMmfseYsLDEH8zstL1sZHh\niI0Mx7iFS2Ft5wC5XBsGRsZVzlnZ82NuXEfq/bsYPmUWDIxN4NXND7aOzoj47aLyMfqGRtDTN4C+\noRFe6tEbj1JTlOv8X3sDvx0PUV7Hdff2TTi6tYKOnl5tD7FaUZfXAwCMf285nFq6Q0dPDy282yHr\n0YM6OQYurdrAo6MPhoyfChf31tU6LkREpPp4GieRhMTExMDW1lbsGGpBJtMCBOF/P2v9/dmXICA1\n4S4AwLapk3K9g2tzAFCue9F6RWkpAMC5lUeNsiXfjXvh8x8kJQAAJvu1q7A8Pzv7mY/X0tKC8I/f\ntdewUdizKQi/HQ9Gr2GjcOHQAfQZ/nqNskqFKr8eAODgV9txct8eZKSloqS4CNo6OjXeVnW86LgQ\nEZHqY9kjkhAnJyd06dJF7BiS8c+CVF7++M8ymUy5/HnrFaUlAAC5ds3ekFf2/Cdv9P8T+hcMjE2q\nvX1TC0t06hOAsz/vR8+hr+F+zG2Ma7O0Rlk1iVivh6grl7F7/SrM3/w5vLv54drFs/gkcFaNtkVE\nRJqFp3FSjfDOgKqnpKQEoaGh6Ny5s9hR1J6towsAIC3hvnJZUnyMcl1l661s7QEAKffiarT/yp5v\n1eTx+sTYmBptHwD6jhyDm3/+hjM//QCvbn413o4mEPv1kBgbA3ObxugSMAgGxib/G10jIiKqBP/F\noFoReCqPyoiPj4dCoUCLFi3EjqL2XNxbo4VXe/x3SxCyHqbjYUoy9m4KQguv9nBu5VHpeo+OnWFp\n2wTfBq1Qrr967mSV91/Z85u18YK9a3N8s+ZDpCcloLysDBnpaSjMz6vyPjy79oC1nQO+XbscfkOG\nV+v4aBqxXw/mNrbIyXiExNgY5GVnITYyvB5/WyIikhKWPaoRjuypnujoaABA8+bNRU6ien7YugFR\nV37DiR9244etG3Ap5Bd8u3YFsh49wMJXA5TfAWDzwhlIvX8XczduR1lZGab5d8Gsl3tAV18fczdu\nV27zRevl2jp477PvkJ+Tg2l9u2Ll5DEwNDFD1qMH2DR/WqV5K3u+TCbD4h3fQVtHF7Ne7oG3fFrj\nm9XLUFxUiAOff4JbYVdw7L+7cOKH3Qg7fxp7NwfhQXIiPlu6QLkPmUyGPiNGo03nbjC1sKzjI67a\n1O318JJvL3h364mFwwOw7K0R0DMwRGlJCVZMHI07N65j0/x3AQDzhvbB0T3fPHPZgR1b8N3aj5CR\nlop5Q/rgs6ULcCvsCo7u+Rrb3p9X4+NCRESqTSZwaIZqIDo6Gi1btsS1a9fg7e0tdhwCsHHjRqxf\nvx7JycliR6l3Hq1bo03PALw+a6HYUdTazzu3wcW9Nby796z2c/dv24irxw8h+q+/6j5YNd26dQse\nHh7YeOg0nFq4ix2H/iUxNgazB/ohIiICbdu2FTsOEZEmucUbtFCNcGRP9cTExMDNzU3sGFSJ9KQE\nvNvnxddVfnb6D9jYN62X/Z/56Qd06fcybl8Lxc3Q3zFs8vR62Q9VjdivByIikjaWPaqRJ2WPA8Oq\nIzo6mtfrqQEb+6Y4cFu80deY8DDs+OD/0KpdR8xZt1W0HPSY2K8HIiKSNpY9qhWWPdURGxuLvn37\nih2DVNyU5UGYsjxI7BhERETUAHiDFqoRnsapWgRBQGpqKhwcHMSOQkREREQqgmWPaoSncaqWrKws\nFBcXw8bGRuwoRERERKQiWPaoVlj2VEN6ejoAoHHjxiInISIiIiJVwbJHNcLTOFVLWloaAJY9IiIi\nIvoflj2qEZ7GqVrS0tKgpaUFKysrsaMQERERkYpg2aNaYdlTDWlpabC0tIS2Nm+wS0RERESPsexR\njfA0TtXy4MED3pyFiIiIiCpg2aNa4cieasjKyoK5ubnYMYiIiIhIhbDsUY1wZE+15Ofnw8jISOwY\nRERERKRCWPaoRniDFtVSUFDAskdEREREFbDsUa2w7KmG/Px8GBoaih2DiIiIiFQIyx7ViFwuBwCU\nl5eLnISAxyN7LHtERERE9E8se1QjOjo6AICSkhKRkxDAa/aIiIiI6Gkse1Qjurq6AFj2VAVH9oiI\niIjo31j2qEZY9lRLYWEh9PX1xY5BRERERCpEW+wApJ5Y9lSLIAgaNx1Gwp1oXD56SOwYGuv+nWix\nIzwl/NfzSIqNETsG/cujtFSxIxARaSyWPaoRuVwOuVzOsqciZDKZRt0ZVSaT4fcTIfj9RIjYUTRa\ni5YtxY5QwbdBK8SOQEREpFJY9qjGdHR0WPZIFFGRkfWy3R9//BFvvPEGJkyYgO3bt0NLi2e6qwN3\nd3eV/LCjtLQUHh4e6Nq1K7799lux4xARkQZi2aMa09XVZdlTEZo2slcffvjhB4wdOxaTJk3C9u3b\nNe60WKp7O3bswP3793H8+HGxoxARkYbix9ZUYyx7qoNlr3b27t2LsWPHYs6cOfjss89Y9KjW8vLy\nsGrVKsyaNQuurq5ixyEiIg3Fskc1xrKnOlj2am7nzp148803MX/+fKxbt07sOCQRa9euRXFxMRYt\nWiR2FCIi0mAse1Rjurq6KC0tFTsGgWWvpnbs2IGpU6di4cKFWLNmjdhxSCLS09OxefNmvPfee7C0\ntBQ7DhERaTCWPaoxjuypDh0dHSgUCrFjqJUNGzZg2rRpWL58OYse1ally5bB1NQUM2bMEDsKERFp\nON6ghWqMI3uqw9DQEAUFBWLHUBtr167FokWLsHHjRsyZM0fsOCQh0dHR+Oqrr/D555/D0NBQ7DhE\nRKThWPaoxjiypzpY9qouKCgI7733HrZs2YKZM2eKHYckZvHixWjevDnefPNNsaMQERGx7FHNseyp\nDpa9qvnggw+wcuVKbN26FdOmTRM7DknMlStX8NNPP+HQoUPQ1uY/r0REJD7+a0Q1xrKnOgwNDVFY\nWCh2DJUlCALmz5+PTz75BN988w3eeustsSORBC1atAjdu3fHoEGDxI5CREQEgGWPakFPTw9FRUVi\nxyA8LntpaWlix1BJgiBgzpw52LZtG3bt2oWxY8eKHYkk6PDhwzh79ix+/fVXsaMQEREpsexRjRkb\nGyMvL0/sGASexvk8giBg5syZ2LlzJ/bt24dXX31V7EgkQWVlZVi8eDFGjBiBrl27ih2HiIhIiWWP\naszMzAzp6elixyAARkZGyM/PFzuGSikrK8OkSZOwd+9e7Nu3D0OHDhU7EknUt99+i9u3b2Pfvn1i\nRyEiIqqA8+xRjZmamiInJ0fsGATA3NwcGRkZYsdQGWVlZRg/fjx++OEHHD58mEWP6k1RURE+/PBD\nTJ48Ge7u7mLHISIiqoAje1RjLHuqw8LCAo8ePRI7hkooLS3F6NGjcfToURw6dAj+/v5iRyIJ27x5\nMzIyMvDBBx+IHYWIiOgpLHtUYyYmJix7KsLCwgLZ2dlQKBQafcv3kpISjBo1CidPnkRwcDB69eol\ndiSSsMzMTKxbtw7z58+Hra2t2HGIiIiewtM4qcY4sqc6LC0tIQgCsrKyxI4imuLiYowYMQJnz57F\nyZMnWfSo3q1cuRJyuRzz588XOwoREdEzsexRjZmZmbHsqQgLCwsA0NhTOQsKCjBo0CBcvHgRJ06c\nQJcuXcSORBJ37949bNu2DR988AFMTU3FjkNERPRMLHtUY5aWllAoFMjOzhY7isaztLQEAI28SUt+\nfj4GDx6M8PBwnDt3Dp06dRI7EmmApUuXws7ODu+8847YUYiIiJ5Lcy/uoVqzsbEBAKSlpcHMzEzk\nNJpNU0f2srOzMWDAAMTFxeH06dNo27at2JFIA0RERGDPnj3473//C11dXbHjEBERPRdH9qjGGjdu\nDOBx2SNxGRoawsTEBKmpqWJHaTBZWVkICAhAfHw8ix41qMDAQLRr1w6vvfaa2FGIiIheiCN7VGPW\n1tbQ0tJi2VMRdnZ2SE5OFjtGg8jMzERAQADS0tJw8eJFNG/eXOxIpCHOnz+PY8eO4dSpU5DJZGLH\nISIieiGWPaoxbW1tWFhYID09XewoBKBJkyZISUkRO0a9S09Ph7+/P3JycnD27Fm4urqKHYk0hCAI\nWLRoEQYMGIA+ffqIHYeIiKhSLHtUKzY2NhzZUxGaMLKXmpoKf39/lJaW4tKlS3BwcBA7EmmQH374\nAVeuXEFoaKjYUYiIiKqE1+xRrTRu3JhlT0XY2dlJemQvISEBPXr0QFlZGc6ePcuiRw2qtLQUS5cu\nxdixY/HSSy+JHYeIiKhKWPaoVuzt7ZGYmCh2DMLj0zilOrJ379499OrVC9ra2jhz5gzs7OzEjkQa\nZseOHUhMTMSKFSvEjkJERFRlLHtUKy4uLoiPjxc7BuFx2UtLS0NZWZnYUepUfHw8evXqBVNTU1y4\ncAFNmjQROxJpmLy8PKxatQozZsyAk5OT2HGIiIiqjGWPauVJ2RMEQewoGq9p06ZQKBSSOpXzr7/+\ngq+vLywsLHDy5ElYWVmJHYk00Nq1a1FcXIxFixaJHYXo/9m787CoyoYN4Pcs7AiIC4qCyKYoyCKE\nCqKISynilqjlnktlaWV9mJZLlGmamZmmbyaaZom55IaBu+KGgiKCIgKKuyLIIus83x++zhu568wc\nlvt3XXOJnMN5bnCoueec8zxERM+FZY9eStOmTXHv3j3OyFkJ2NnZAUC1OdOanJyMwMBA2NnZYdeu\nXahTp47UkagGunHjBubPn49PP/2Uz0EiIqpyWPbopTRt2hRA9SkYVZm1tTUMDAyQkZEhdZSXlpCQ\ngICAADg6OmL79u0wMzOTOhLVUNOmTYOZmRnee+89qaMQERE9N5Y9eimNGzeGnp4ey14lIJfLYWtr\nW+XL3okTJ9C5c2e0bNkS27ZtQ61ataSORDXUuXPnsGzZMoSHh8PY2FjqOERERM+NZY9eikKhgK2t\nLdLS0qSOQrh/KWdVLntxcXHo0qULfHx8sH37dpiamkodiWqwTz/9FI6OjhgyZIjUUYiIiF4IF1Wn\nl+bi4oKkpCSpYxDul70Hxfv69es4c+YMUlJSYGFhgUGDBkmc7skOHDiA7t27IyAgAOvWrYOhoaHU\nkaiG2LFjB7p06QK5/H/vfx49ehQbNmzA5s2boVTyf5VERFQ18f9g9NJatWqFTZs2SR2jRhJCIDMz\nEykpKThz5gzOnj2LlJQUmJmZIS8vT71fZV8bbO/evQgODsarr76K3377DXp6elJHohqivLwcISEh\ncHJywrx589C1a1cAwKRJk+Dv748ePXpInJCIiOjFsezRS3Nzc1NPTW5gYCB1nBrj/Pnz8PDwQEFB\nAQBAX18fKpUKZWVlFfaTyWRwc3OTIqLajRs3UL9+/Udui4qKQt++fRESEoJVq1bxLArp1IULF1BS\nUoLk5GR069YNHTp0QK9evbB7924cPHhQ6nhEREQvhffs0Utzc3NDWVkZUlJSpI5Sozg6OqJ79+5Q\nKBQAgJKSkoeKHnD/7F+rVq10HU+tuLgYr7zyCiIjIx/atm3bNvTp0wd9+vRh0SNJJCcnAwBUKhUA\n4ODBg/joo4/QvHnzx75BQUREVFWw7NFLa9asGQwMDJCYmCh1lBpn9uzZkMlkT9zHyMhIvUSGFJYt\nW4bMzEwMGjSowuW+mzdvRt++fTFkyBD8+uuvLHokieTkZOjr66v//uANk7S0NDRv3hxjxozBtWvX\npIpHRET0Ulj26KUplUo0b96cZU8CTZs2xdixY594j5urq+tTC6G2lJSU4KuvvoJMJoNKpcLrr7+O\nLVu2YO3atejXrx9GjBiBJUuWVJgYg0iXkpOT1Wf1/qm0tBTl5eWIiIiAg4MDZs6cKUE6IiKil8NX\nWKQRHh4eOHHihNQxaqRp06Y9tuzp6emhdevWOk70Pz///DOuXbsGIQSEECgvL0fv3r3xxhtvYPz4\n8Vi0aJFkRZQIAE6ePPnIy58fKC8vR2lpKVq0aKHDVERERJrBskca0bZtWxw+fPiJL5pIO+rVq4ew\nsDD1vXv/JISQbHKWB2f1hBAV8gghIJfL0aNHDxY9kpQQAufOnXvsdoVCAQMDA2zbtg29e/fWYTIi\nIiLNYNkjjfDz80N+fj4v5ZTIxIkTUbt27YfKU1lZmWRl759n9f5JpVKhvLwcr732Gvbt2ydJNiIA\nuHz5MgoLCx+5TU9PDxYWFoiNjUXnzp11nIyIiEgzWPZII1q2bAlLS0tOVS4RExMThIeHP1T2pFp2\nobi4GOHh4Q8VvQceLBHx2muv4ejRozpOR3Tfg5k4/01PTw8NGjTA4cOH4eHhoeNUREREmsOyRxoh\nk8ng6+uL2NhYqaPUWKNHj4a9vX2FyU6srKxgYWGh8yzLly/HjRs3Hlv2gPv3QhUWFqJnz564deuW\nDtMR3ZecnPzQ/a56enpwcXFBXFwcHB0dJUpGRESkGSx7pDHt2rXjmT0JKRQKzJo1Sz2zoEwmg6en\np85zlJaW4ssvv3xi0VMqlTA0NMT48eMRFxeHunXr6jAh0X3/PrOnVCrRpk0b7N+/n2vsERFRtcCy\nRxrj5+eHixcv4uLFi1JHqbH69esHHx8fKJVK6OvrS1L2li1bhqtXrz5U9mQyGRQKBWrXro0pU6bg\n8uXL+P7772FjY6PzjEQAcOrUKZSWlgK4/2bJa6+9hr///htmZmYSJyMiItIMlj3SmHbt2sHU1BQ7\nduyQOkqNNnfuXJSVlaG4uBiurq46HftRZ/XkcjlkMhkaNWqEb7/9FpcvX8b06dNhaWmp02xE/5aS\nkgLg/hsRY8aMwcaNG2FoaChxKiIiIs1RSh2Aqg8DAwN07NgR27dvx+jRo6WOU6Pk5uYiPz8feXl5\nMDExQZs2bXD48GEUFBQgJiYGJSUlKCgoqPA1FhYWFSZ0MTc3h1wuR+3atWFqaopatWrByMjouXL8\n8ssv6rN6CoUC5eXlcHV1xWeffYZ+/fpx8XR6Ifn5+cjNzUVOTg6KioqQm5urvly5uLi4woyaxsbG\nMDAwAHD/jQZzc3MYGhrCwsIC5ubmMDU1BQBkZ2cjOzsbADB9+nRMnTpVx98VERGR9snEk26sIXpO\nixYtQlhYGG7duqV+wUXP79atW8jIyMCVK1dw/fp1XLt2DTdv3sSNGzfUHz8oeLm5uVrLoVAoYGZm\npn6h3LBhQ9SrVw/16tVTf1y/fn3Y2tqiUaNGaNGiBa5cuQIA6NSpEyZPnoygoCCt5aOq7erVqzh/\n/jwuXryIa9euISsrC1evXsXly5dx/fp1ZGdnIzc3V6PrdyqVSpibm8PIyAiXL19G69at4e/vj8aN\nG6NBgwawtbWFo6MjGjZsqLExiYiIJJLMskcalZ6eDnt7e+zcuROdOnWSOk6llpmZiZSUFJw5cwbp\n6enIyMhQ/5mfn6/ez8zM7JEly8LCAqampjA3N4eZmRlMTU3VZ+SUSiXWrFmDMWPGAPhfaXtApVJV\nKIlCCOTk5Kj/zMvLQ15envpsYU5ODu7cufPI0llUVFTh+6pTpw68vb3h6ekJBwcHtGjRAi1atJBk\nVlCSnkqlwvnz55GQkIDExESkpqaqH3l5eQD+t9TBg8LVuHFjWFlZwdLSEubm5uo3G8zNzWFsbAxT\nU1P1LJpKpRK1atVSj5eXl6cuh6WlpcjPz0dhYSFyc3PVZwdzc3Oxc+dOFBQUwMjISF00r1+/jpKS\nEgBArVq14OTkpH64ubnBw8MDjo6OPENNRERVBcseaZ6LiwuCg4MxZ84cqaNUCnfv3kVcXBzi4uKQ\nnJyMpKQkpKSkqF/oWllZwcHBAU2bNoWdnV2FR+PGjV/4HiIhxEPr7mlDTk4OMjMz8d1338HW1hZ3\n795FRkYGMjIykJaWpi6uDRs2VBc/Nzc3+Pj4wNXVFUolryavTjIyMrBv3z4cO3YMCQkJOHnyJPLy\n8qBUKuHk5IRmzZrByckJzs7O6iJlbW2t85wlJSXQ19d/6PNXrlxRl9Fz584hNTUVZ8+eRWpqKsrK\nylCrVi24u7vDw8MDPj4+CAgIgJ2dnc7zExERPQOWPdK8iRMnIioqCklJSVJH0bny8nKcOHECR44c\nwbFjx3Ds2DGcPXsWKpUK1tbWcHV1RYsWLeDi4qIuPtV5ohIhBDIzMyuU3NOnTyMpKQn5+fkwMjKC\np6cnXnnlFfj4+KBdu3Z84VzFXLhwAbt27cK+ffuwd+9eXLx4EQYGBvDw8ICnp6f64ebmVqUnPykq\nKkJiYiLi4+PVj4SEBBQXF8PW1hYdOnRAQEAAOnXqBHt7e6njEhERASx7pA179uxBYGAgkpOT0bx5\nc6njaN2FCxcQExOjfty5cwdmZmZwc3ND69at1fcE8QXg/5SXlyMlJQXHjx+v8CgqKkLDhg3h7++P\nzp07o1u3bmjSpInUcekfysvLkZCQgM2bN2PLli04fvw4jI2N4enpqf538/Pze+7JfaqisrIynDx5\nEjExMThw4AD279+P3Nxc2NvbIzg4GD179kSHDh0eWridiIhIR1j2SPNUKhVsbW0xatQoTJ8+Xeo4\nGldQUIDt27dj06ZN2L59O27fvo26deuiQ4cOCAwMRGBgIFxcXHRyCWV1UlRUhCNHjmD37t3YtWsX\njhw5gpKSEri4uKBnz57o3bs3fH19eb+UBIQQiI2NxcqVK/Hnn3/i9u3bcHJyQnBwMIKDg9G+fXsW\nGty/R3D//v3YunUrNm/ejNTUVNSpUwf9+vXDkCFD4Ofnx/8uEBGRLrHskXZ88MEHiIqKUq9jVdXd\nuXMH69evx8aNGxETE4PS0lL4+/sjJCQEQUFBcHNzYwnRsMLCQhw4cADR0dHYuHEjzp8/jwYNGiAk\nJAR9+/ZF586doVAopI5ZrWVkZCAiIgKrVq1CWloaWrVqhcGDB6NXr15wdnaWOl6ld/bsWWzatAmr\nV6/GqVOnYG9vj8GDB2PEiBG8XJmIiHSBZY+04/Dhw2jbti1OnDgBT09PqeO8EJVKhV27dqnPZqhU\nKvj7+yM4OBgDBgxAgwYNpI5Yo1y4cAGbN29GZGQkYmNj0aBBA/Tv3x8jR46Eu7u71PGqlePHj+P7\n77/HmjVrUK9ePfTv3x/9+/eHv7+/1NGqrKSkJERGRmLFihW4ePEiunfvjgkTJqBz585SRyMiouqL\nZY+0x9HREf369cPs2bOljvJcrl69ioULFyIiIgJXr16Fv78/hg8fjv79+1eY4p2kk5aWhpUrVyIi\nIgIXL16Er68v3n77bbzxxhuPnGGRnk4IgQ0bNmDmzJk4fvw42rZtiw8++AB9+/bljKkaVFZWhvXr\n12P+/Pk4dOgQvLy8MHnyZPTt25eXeBIRkaYl87oz0pr+/ftjzZo1qCrvJ6SkpGD06NFo2rQpfvnl\nF4wcbw0WKAAAIABJREFUORLnzp3Dvn37MHLkSBa9SsTBwQEzZsxAeno6YmJi4ODggDFjxsDe3h5z\n5szB3bt3pY5YpezZswdt27bF66+/Dnt7exw+fBixsbEIDQ1l0dMwpVKJ0NBQxMbG4vDhw7C3t0f/\n/v3Rtm1b7NmzR+p4RERUzbDskdYMHDgQly5dwsGDB6WO8kTnzp1Dv3790LJlS+zfvx8//PAD0tPT\nER4eDkdHR6nj0RPI5XIEBQVh9erVSEtLw4ABAxAeHg5bW1tMnz4dBQUFUkes1DIzMxEcHIzAwECY\nm5sjLi4Oa9euha+vr9TRagRfX19ERkYiLi4O5ubmCAwMRHBwMDIzM6WORkRE1QTLHmmNu7s7vLy8\n8J///EfqKI+UnZ2NDz/8EK6urkhNTcWff/6JM2fOYPTo0VV6PbCaysbGBt9++y0uXryI//u//8P8\n+fPRrFkzREREQKVSSR2vUhFCYPHixXBzc1MvHbJjxw54eXlJHa1G8vLywo4dOxATE4MLFy7Azc0N\nixcvrjJXRRARUeXFskdaNXr0aERGRiI7O1vqKBWsWLECTk5OWLNmDRYuXIj4+Hj07t2bM2pWAxYW\nFpg8eTJSU1MREhKC0aNHw9fXF0lJSVJHqxRu3bqFzp07Y/z48Xjvvfdw4sQJBAUFSR2LAAQFBeHE\niRMYN24cxo8fjy5duuDWrVtSxyIioiqMr2xJqwYPHgylUolff/1V6igAgJycHAwcOBAjR47EsGHD\nkJqaijFjxnAK/2qoXr16WLRoEU6ePAl9fX34+Phg4cKFNfpsSVJSEnx9fZGeno7Dhw9j5syZPItd\nyRgaGuLrr7/GoUOHcOHCBb5RQUREL4Vlj7TK1NQUAwcOxJIlSyR/kX3ixAm4u7tj//792LFjB+bN\nm8dJV2qAFi1aYO/evQgLC8OHH36IkJAQ5OfnSx1L53bv3o127dqhUaNGOHLkCFq3bi11JHoCb29v\nHDlyBI0aNUK7du2wa9cuqSMREVEVxKUXSOsSEhLg6emJ/fv3S7ZO1759+xASEgJfX1+sXr0adevW\nlSQHSSs2NhZ9+/aFnZ0dtm3bBktLS6kj6URcXBw6deqE7t27Y+XKlVyeogopKSnB8OHDsXnzZuza\ntQs+Pj5SRyIioqqD6+yRbnh7e8PFxUWSyzmjoqLQt29fBAcHY9WqVXyhW8OlpqaiS5cuMDMzQ0xM\nDOrXry91JK06d+4c/P394ePjg40bN0JPT0/qSPScSktL0bt3bxw9ehQHDx6Es7Oz1JGIiKhq4Dp7\npBtvv/021q1bh+vXr+t03LS0NAwcOFC95h+LnubMnj0bZmZmaNCgAYD7s6/KZDJERERIG+wpnJyc\nsH//fhQWFuKNN96o1jN1qlQqDBs2DHZ2doiMjKy2RS80NBQKhQIff/yx1FG0Qk9PD5GRkbCzs8PQ\noUNRXl4udSQiIqoiWPZIJ4YMGQILCwssXLhQZ2MWFxdjwIABaNKkCZYsWSLJJCwxMTEVvud//70q\nCwsLw7x589R/P3nyJOrUqSNhomdnY2OD9evXIzY2FuHh4VLH0ZoffvgBJ06cwPLly2FsbKzTsXX5\n3F+7di3at2+vlWNXFsbGxvj111+RkJBQbf4bQkRE2seyRzphYGCAd955B4sWLdLZ5BizZ89Gamoq\n1q9fL9mMg/9eY1DXaw6WlZXhl19+wfnz57VyfJlM9tDnqsrZo1atWuHbb79FeHg4EhMTpY6jcQUF\nBZg6dSrCwsLQsmVLnY8v9XO/OmrevDnCwsLw+eef18hJhoiI6AUIIh25ffu2MDExEQsWLND6WHl5\necLMzEx8+eWXWhtjxIgRQiaTiTlz5gghhPjuu++EsbGxaN26tRBCiJEjRwqZTCYACACie/fuFf5+\n9epVkZaWJrp27SrMzMyEnZ2d+Pnnn4UQQkyePFnI5XLx6aefiq+++krY2dmJhISEp2Y6ffq0CAoK\nEmZmZsLU1FRYWVmJd955R709IyNDBAcHC1NTU1GvXj3Rr18/ceXKlWfefu3aNRESEiKMjY2FoaGh\nqF+/vrCyslJvr1OnjrC1tVWPHxgYKJKTk4UQQoSGhgoAwtjYWMydO1cIIcRbb70l9PT0hLe3txBC\nPPbnoS0qlUp4enqKAQMGaHUcKSxfvlzo6+uLmzdvavzYlfG536FDB9GnTx/xxhtvCEtLS9GwYUOx\ndetW9fa5c+cKa2troaenJ2xtbcXatWuFEEJMmzZNyGQyERgYKOzt7YWxsbHo0KGDSE1Nfabtz/K8\n1qTbt28LQ0NDsXz5co0fm4iIqp0zLHukU++++65o2rSpKC0t1eo4K1asEAYGBuL27dtaHadZs2bq\nF7xCCBEeHq5+wSuEEE2aNBE//PDDI/9eVlYmHBwcxLvvvityc3PFjh07hEwmE0eOHBFC3H/xGhQU\nJPbu3Svmzp0r4uPjn5qnVatWYtCgQSI3N1dcvHhRtGzZUowcOVK93dfXV4SEhIirV6+KzMxMERQU\nJHx9fZ95u7e3t/Dz8xMZGRmitLRUzJs376GyN3PmTFFUVCSysrJE586dhZOTkygrKxNCCOHs7CyG\nDh1aIXP79u3FvXv3nvrz0Jbly5cLAwMDcefOHa2Oo2uBgYEiNDRUa8evbM/9Dh06iLFjx4qCggJR\nUFAg+vbtW6FsffDBB+LkyZPi3r17YuLEiaJJkybqbe7u7mLcuHEiPz9fXL58WQQFBQk3N7dn3v6k\n57U2DBw4UHTo0EErxyYiomrlDC/jJJ36+OOPcfHiRaxfv16r4+zevRt+fn6Vemr9o0ePIi0tDZMn\nT4aZmRm6du0KR0dHxMTEqPfx8PBAQEAAJk6cCA8Pjycer6ysDImJiejVqxfMzMxgY2MDf39/9SWc\n8fHxOHLkCL788ks0aNAAtra2mDlzJo4cOYL4+Pinbo+Li0NcXBy++eYbNGnSBEql8pHrFDZs2BAG\nBgZo1KgRvvnmG6SmpuLIkSMAgFGjRuHPP/9UX4J28uRJuLq6wtDQ8Jl+HtoQEhKCkpISHDx4UKvj\n6FJZWRkOHTqEHj16SB3lkTT93H/A1NQUxsbGMDY2Rrdu3ZCVlaXe9t1336FVq1YwNDREmzZtHpos\nqkGDBjAxMYG1tbX60t74+Phn2v6k57U29OjRA4cPH0ZpaalWjk9ERNUHyx7pVNOmTdG7d2/MmTNH\nq+OkpqbC1dVVq2O8rIyMDABA48aNIZPJIJPJkJqaijt37rzQ8ZRKJezs7LBp0ybcvXsXly5dwt69\ne+Hp6Qng/sykAGBvb6/+mubNm6u3PW37uXPnANyfdfNZOTk5AQAyMzMBAMOHD0dJSQnWrVsHAFi1\nahVGjhwJQPM/j2dlaWkJa2tr9fdXHVy7dg1FRUXqn39lo4t/a7lcDvGPlYXmzJkDZ2dnmJiYIDQ0\ntMK2f2vWrBkAID09/Zm2P+l5rQ1OTk4oLi7GlStXtDYGERFVDyx7pHOffPIJ4uLisHPnTq2NUVhY\nCBMTE60dXxMeLAORm5sLIYT68TJF+Ndff8XWrVtRt25deHl5wcfHB1988UWFff75IvfBsgP/nGjl\ncdtLSkoAPN8ELPfu3QMA9UyQ9erVQ69evbBixQqoVCqcPn0a3t7eALTz83hWJiYmKCgo0Po4ulJU\nVAQAkk1M9DS6/rfes2cPwsLCMHPmTFy9ehW///77E/d/cMZMqVQ+0/YnPa+14cHv04PfLyIiosdh\n2SOd8/X1RZcuXTB16lStjWFpaYkbN25o7fgPyGSyF16nzdbWFgCQnJyskSxCCCxatAhfffUVSkpK\ncPPmTaxcuRJmZmYAAEdHRwDAhQsX1F+TkpKi3va07TY2NgDunzV9Vg9muXxwJgQARo8ejb1792L5\n8uXo2rWr+vOa/nk8KyEEbty4UWWWjXgWFhYWAKDVs6KV6bn/NMnJybC2tsbrr78OMzMzyOVP/l/f\n6dOnAeCxZ0Yftf1xz2ttuH37NgCgdu3aWh2HiIiqPpY9ksSMGTMQGxurtfuxPD091feJaVPDhg2x\nc+dO5OfnIycnB1evXq2w3cjICMeOHUNxcTGKiooq/N3V1RXNmzfHRx99hIyMDJSXl+PKlSvIy8t7\noSwymQyGhoZ4//331ZfGGRgYwN/fH5mZmfDw8ECbNm3w+eef49q1a7h06RKmTJmCNm3awN3d/anb\nAwIC0LhxY3z88cfq7Vu2bHkoR15eHsrKypCRkYEpU6agc+fO6stBAaBLly5o0qQJPv74YwwePFj9\neW9vb43+PJ7V2bNnkZOTAy8vL62Oo0t169ZF/fr1K9xzpmmV6bn/NNbW1rh58yaSk5ORnZ2NuLi4\nh/YpKChAaWkpLl68iM8//xwdOnSAi4vLM29/3PNaG+Lj41G3bl1YWVlpdRwiIqoGJJgVhkgIIUS3\nbt1E27ZttXLsvXv3CgDi1KlTWjn+A1FRUcLKykqYmZmJXr16iUGDBgm5XC7ef/99IYQQ8+bNE0ZG\nRsLW1lYcOHDgob+fP39edOzYURgaGgpzc3MRGhoqrl27JqZMmSIUCoUwMDCoMJvm04wYMaLCNPX3\n7t0T3bt3F2+//bYQQoj09HTRvXt3YWxsLExMTESPHj1Eenq6ev+nbY+Pjxdt2rQRJiYmomXLlmLY\nsGECgBg0aJAQQoi3335bODg4CD09PWFqaioGDBjwyFkuw8PDRZ8+fR76/ON+Htr02WefCWtra/WM\nodXF8OHDhY+Pj9aOX5me+w++xsjISPz0008iOjpa1K5dWwAQgwcPFkVFRSI4OFgYGRkJd3d38cUX\nXwgAomvXrkKI+7Nt1q5dW+jr6wsjIyPRu3fvCktWPG37A497XmvaK6+8IoYNG6b1cYiIqMo7IxPi\nCXepE2lRXFwcXnnlFURFRWn8sichBFq1agUXFxesXbtWo8eurI4cOYKRI0ciKSmpwudHjBgBmUyG\nX375RaJkD5s9ezY8PDzQrVs3SXPk5OTA3t4eEyZMwLRp0yTNomn79+9HQEAATp48iVatWkkdp1Lz\n8PBAaGgoJk+e/ELbH9DF8/r06dNwc3PDnj170KFDB62NQ0RE1UIyL+MkyXh7e+PVV1/VyotsmUyG\n2bNnY926ddWi7GVkZKgvzXzc4/r160hPT8euXbtQXFyMmzdv4qeffsJvv/2G0NBQqb8FLF++HHfv\n3kVUVBT27dsnedEDgDFjxsDQ0BAffPCB1FE0zt/fH61atcInn3widZSX8izP/Qeze76Mp91/+Ljt\nun5ef/zxx3B1dUVAQIBWxyEiompC4lOLVMPFxcUJmUwmtm/frpXjjx8/XpiamoqUlBStHL+yWbx4\nsWjWrJkwNDQUpqamon379mLz5s1SxxJCCDF27Fihp6cnOnbsKLKysqSOI3744Qchl8tFdHS01FG0\n5siRI0KhUIgVK1ZIHaXSevfdd4VMJhNGRkZi3Lhxz71dl8/rFStWCLlcLg4cOKDVcYiIqNrgZZwk\nvZ49e+LGjRs4fPhwhSUANKGkpAT+/v7Izs5GdHQ0mjZtqtHjU9X0+++/Y+jQoZg2bRqmTJkidRyt\nmjBhAiIiIrBnzx71motU9SQkJKBjx44YNmwYvv/+e6njEBFR1ZDMskeSi4+Ph7e3N/744w+8/vrr\nGj9+dnY2unfvjszMTPz9999wc3PT+BhUdaxcuRJvvfUW3nnnHXz//fcaf4OhsiktLUVISAiOHz+O\n/fv3V1gGg6qGCxcuwM/PDy1atMC2bdtgYGAgdSQiIqoaeM8eSc/T0xODBg1CWFiYeuFuTbK0tMSO\nHTvg5OSEjh07YsOGDRofgyq/4uJiTJw4EcOHD8fUqVOxYMGCal/0AEBPTw+RkZGws7NDt27d1Gsf\nUtWQmJiIoKAgNG7cGBs3bmTRIyKi58KyR5XCl19+icuXL2Pp0qVaOb65uTmioqLQr18/9O3bF2PG\njEFBQYFWxqLKJykpCb6+vvj555+xYsUKfP7551JH0ilTU1NERUWhadOm8PPzw19//SV1JHoGf/31\nF/z8/NCkSRNERUWhVq1aUkciIqIqhmWPKgU7OzuMGzcOX3zxBe7evauVMYyNjbF06VKsX78eGzZs\ngIeHBzZt2qSVsahyKCwsxIwZM+Dj4wNjY2PEx8djyJAhUseShKWlJf7++28MGjQIffr0waeffoqi\noiKpY9EjFBUVYdKkSejTpw8GDRqE6Oho1KlTR+pYRERUBbHsUaXx2Wefoby8HHPnztXqOH369MHJ\nkyfh7e2NPn36oFOnTkhISNDqmKRbKpUKK1euhLOzM7777jvMmDED+/btg729vdTRJKWnp4clS5Zg\nyZIlWLRoEby8vHDo0CGpY9E/HDp0CJ6enli8eLH630pPT0/qWEREVEWx7FGlUbt2bYSFhWHu3LnI\nysrS6ljW1tZYs2YNYmNjce/ePbRu3RoDBw7E8ePHtTouaVdZWRl+++03eHl5YeTIkejZsyfOnTuH\nTz75BEqlUup4lcaoUaOQmJgIW1tbtG/fHqNHj9b67xw9WVZWFkaPHo327dujSZMmSExMxKhRo6SO\nRUREVRzLHlUq48ePR7169fDFF1/oZLw2bdogNjYWv//+O1JTU+Ht7Y1OnTph+/bt4ES1VUd+fj7m\nz58PR0dHDBs2DC1atMDJkyexePFi1K9fX+p4lZKtrS2ioqIQERGB6OhoODs7IywsDNnZ2VJHq1Gy\ns7MRFhYGZ2dnREdHIyIiAlFRUbC1tZU6GhERVQNceoEqnRUrVmDUqFFISEhAy5YtdTr2gQMHMHv2\nbGzduhWNGzfGG2+8gbFjx3J9vkrq+PHjWLlyJVatWoXi4mK8+eabmDhxIpydnaWOVqWUlJQgIiIC\nU6dORWFhIQYNGoSPPvqIyzRoUXp6OpYsWYKlS5dCLpfjk08+wYQJE2BoaCh1NCIiqj64zh5VPiqV\nCt7e3qhbty7+/vtvSTIkJiZi2bJlWL16NbKzs9G5c2cMHToUwcHBMDc3lyQT3XfhwgX88ccfWLFi\nBc6ePYtWrVphxIgRGDJkCCexeEl3797F0qVLsXDhQmRlZaFXr14YN24cOnbsCLmcF4K8LJVKhb17\n92LhwoXYtGkTGjVqhPfeew9jx46FmZmZ1PGIiKj6Ydmjyik2Nhb+/v7YsGEDevXqJVmOkpISbN26\nFREREdi+fTtkMhk6duyIPn36oGfPnmjUqJFk2WoKIQSOHz+OTZs2YdOmTUhMTETdunUxaNAgjBgx\nAp6enlJHrHbKysqwYcMGzJ8/H7GxsbC1tcWbb76JwYMHo0WLFlLHq3LOnDmDVatWYfXq1bh48SLa\ntWuHCRMmoG/fvryXlIiItIlljyqvQYMG4ejRo0hKSqoUlzbduXMH27Ztw8aNGxEVFYWCggK0bt0a\nQUFBCAwMhL+/P0xMTKSOWS1cvnwZu3btwu7duxEdHY2srCw0adIEvXr1Qq9evRAQEMAXyTqSnJyM\nVatWYdWqVbh48SI8PT0REhKCnj17wsvLq0YsTP+8hBA4ceIEtmzZgk2bNiE+Pp6FmYiIpMCyR5VX\nVlYWmjdvjilTpuDTTz+VOk4FRUVF2LlzJ7Zt24bdu3cjOTkZ+vr6eOWVV9CpUye0bdsWPj4+vKzw\nGQghcP78eRw7dgz79+/H7t27cfbsWRgYGMDX1xedO3dGcHAwz+BJTKVSYdeuXQgPD8ehQ4dQWloK\na2tr9OjRA926dUP79u1r9GQ4N2/exP79+7Fjxw5s2bIFV65cQePGjREcHIwBAwYgICCAl8ISEZGu\nsexR5RYeHo5vvvkGZ8+ehbW1tdRxHuvq1avqM1F79uxBWloaAMDe3h4+Pj7w8fFB69at4ebmVqML\noEqlQkZGBk6dOoVjx46pHzk5OdDT00Pr1q0RGBiITp06oV27djA2NpY6MuH+ZZ0rVqzAF198gZs3\nb+K9995DSEgIdu/ejc2bN+PEiRMoLy+Hi4sLAgIC0L59e7Rt27Zar2t44cIFHDp0CPv378e+ffuQ\nnJwMhUIBLy8v9OzZk29QEBFRZcCyR5VbUVERXFxc0L59e6xcuVLqOM/s1q1bFcrMsWPHcP36dQCA\nlZUVWrZsCRcXF7i6uqJ58+ZwcHBAo0aNqs07/0VFRcjIyEBqairOnDmDM2fOICkpCSkpKSgoKIBc\nLkezZs3URdjHxwceHh4wMDCQOjr9gxACW7ZswZQpU5CSkoIRI0Zg2rRpD73xkpubiwMHDqiLT1xc\nHEpLS2FhYQFPT094enrCy8sLbm5ucHZ2rhSXZT+roqIinDt3DomJiThx4gTi4+MRHx+vfoPC29tb\nXXD9/f05gRMREVUmLHtU+a1btw6hoaHYt28f/P39pY7zwrKystSl559/5ubmAgD09fVhY2MDOzs7\nNGnSBHZ2drCxsUH9+vVhZWUFKysr1K9fH/r6+pJ+H/n5+bh69Spu3LiBmzdv4sqVK7h06RIyMjKQ\nmZmJjIwMXL16Vb2/ra0tXFxc0LJlS7Ro0UL9J2cfrNxiYmIwadIkxMfHo1+/fvjqq6/g5OT0TF9b\nWFiIkydPVihHp0+fRklJCWQyGWxsbODk5KR+2NjYwNraGo0aNULDhg11WvqLi4tx7do1ZGVlqZ/L\nqamp6selS5cghIC+vj5cXV0rlFd3d3eefSYiosqMZY+qhq5duyI7OxtHjx6tNme/Hrhy5QrS09OR\nkZHx0OPy5cu4d+9ehf1r164NKysrmJmZwczMDObm5qhVqxZMTU1hamoKCwsLAICpqSn09PTUX2dh\nYaGeTKO4uBiFhYXqbYWFhSguLkZpaSny8/Nx584d5Ofnqx+5ubm4desWbty48VAeS0tL2NjYoEmT\nJmjatCns7OzUD3t7e5a6Kubw4cOYPHkydu/ejc6dO+Obb77RyOWIpaWlOHfuHM6dO1ehTKWmpuLa\ntWtQqVTqfR+8wVG7dm2Ym5vDwsIC5ubm6ue6UqlErVq11PvXrl1b/fGdO3fUH+fl5aGsrAx5eXnI\nzc1Fbm4ucnJykJubizt37uD69eu4ceOGen+5XI4GDRpUKKJOTk5wdnaGs7Nzhd8nIiKiKoBlj6qG\nU6dOoXXr1liwYAHeeecdqePoVH5+Pq5du6Y+k3bt2jVcv34deXl5uHv3LnJycpCfn4+8vDx1MQPu\nX1r34AW0EAI5OTnqY/77xbKBgQGMjY2hUChgZmYGCwsLmJqaqkukubk56tSpg/r166NevXpo0KAB\nrKysUK9ePcnPNJJmJCUlYcaMGYiMjISfnx9mzpyJgIAAnYxdVlaG69evIysrC9euXcOlS5dw/fp1\n3Llzp0JBy8nJQUFBQYU3K1Qqlfo5DwBmZmZQKBQAAGNjYxgYGMDExERdGB/8+eBNExsbGzRo0ACN\nGzeGlZUVZ3klIqLqhGWPqo5PP/0UixYtwunTp2FjYyN1nCrJzc0Nffv2xYwZM6SOQpVERkYGvv76\nayxbtgxeXl74/PPP0bNnT6ljERER0ctLrl7Xw1G1Nn36dDRq1Ahjx46VOgpRlZeVlYWxY8fCyckJ\n+/btw5o1a3DkyBEWPSIiomqEZY+qDAMDAyxbtgw7duzAb7/9JnUcoirp9u3bmDRpEpydnREVFYUf\nf/wRiYmJ6N+/PxdIJyIiqmZY9qhKadu2LcaOHYvx48dXmFiBiJ4sPz8fs2fPhoODA5YtW4Zp06bh\n7NmzGDNmDO9TIyIiqqZY9qjKmTVrFoyNjfHRRx9JHYWo0ispKcHSpUvh6OiIr776Cm+//TbS0tIQ\nFhZWpda7IyIioufHskdVjpmZGRYtWoTVq1djy5YtUschqpRKS0uxcuVKNGvWDB9++CGGDx+OzMxM\nzJo1i8thEBER1RAse1QlBQcHY/DgwXjrrbdw5coVqeMQVRpCCERGRqJly5YYNWoUunbtivPnz2PW\nrFkV1qMjIiKi6o9lj6qsxYsXw9LSEm+88QbKy8uljkMkuZiYGHh7e2PgwIHw8PBAcnIylixZgoYN\nG0odjYiIiCTAskdVlqmpKdauXYujR48iPDxc6jhEkomNjUXHjh3RpUsXWFpa4sSJE1i7di0cHByk\njkZEREQSYtmjKs3NzQ1z5sxBeHg4YmJipI5DpFOJiYkIDQ2Fn58fysrKsH//fkRHR8Pd3V3qaERE\nRFQJsOxRlTdu3DgMGDAAgwcPxrVr16SOQ6R1KSkpCA0Nhbu7Oy5evIiYmBgcOHAA/v7+UkcjIiKi\nSoRlj6qFxYsXw8TEhPfvUbV26dIljB07Fm5ubkhKSsIff/yBQ4cOISgoSOpoREREVAmx7FG1YG5u\njt9//x0HDx7k/XtU7dy6dQuTJk2Cs7MzduzYgR9//BGnTp1C//79IZPJpI5HRERElZRS6gBEmuLj\n44MFCxbgnXfegYuLCwYMGCB1JKKXkp+fjx9//BEzZ86EgYEBpk+fjg8++AAGBgZSRyMiIqIqgGWP\nqpWxY8ciOTkZw4cPh52dHXx9faWORPTcCgsL8Z///AczZ85EWVkZJk+ejPHjx8PIyEjqaERERFSF\nsOxRtfPtt98iLS0NvXv3xtGjR2FjYyN1JKJnUlpaiuXLl2PGjBnIzc3Fe++9h0mTJsHCwkLqaERE\nRFQF8Z49qnYUCgV+++031K1bF7169UJBQYHUkYieSKVSITIyEi1atMD777+P4OBgnD9/HrNmzWLR\nIyIiohfGskfVUq1atbB582ZkZWVh6NChUKlUUkcieqSYmBi0bt0aAwcOhKenJ5KTk7FkyRI0aNBA\n6mhERERUxbHsUbVlZ2eHdevWYcuWLfjss8+kjkNUwYEDBxAQEICuXbuicePGSEhIwNq1a2Fvby91\nNCIiIqomWPaoWgsICMDSpUsxa9YszJ8/X+o4RDh69Ci6dOmC9u3bw8DAAMeOHcPmzZvh5uYmdTQi\nIiKqZjhBC1V7w4YNw927dzFhwgSYmZlh5MiRUkeiGig5ORnTpk3DunXr4Ovri127diEwMFDqWETW\niPNyAAAVC0lEQVRERFSNsexRjfD+++/jypUrGDNmDMzNzdGvXz+pI1ENcfHiRXz11VdYtmwZmjdv\njj/++AP9+/eXOhYRERHVACx7VGPMnDkT2dnZGDx4MOrWrYsOHTpIHYmqsZs3b+Lbb7/F/Pnz0bBh\nQyxatAhvvfUWFAqF1NGIiIiohmDZoxpDJpNh8eLFyMnJQUhICHbt2oXWrVtLHYuqmezsbCxYsADz\n5s2Dubk5FixYgJEjR0Kp5H9uiYiISLc4QQvVKHK5HCtXrsQrr7yC7t27IyUlRepIVE0UFBRg9uzZ\ncHBwwMKFCzFlyhScO3cOY8aMYdEjIiIiSbDsUY1jYGCADRs2wMHBAZ06dUJycrLUkagKKykpwdKl\nS+Hk5IQvv/wSY8eORVpaGsLCwmBkZCR1PCIiIqrBWPaoRjI1NUV0dDScnZ3RsWNHnD59WupIVMWo\nVCpERkbCxcUF77//Pnr27Inz589j1qxZMDc3lzoeEREREcse1VwmJibYunUrWrRogaCgICQmJkod\niaqImJgYeHp64s0334Sfnx/Onj2LJUuWwMrKSupoRERERGose1SjmZiYYMuWLWjZsiULHz1VTEwM\nfHx80LVrVzRr1gxJSUlYuXIl7OzspI5GRERE9BCWParxHhQ+Nzc3BAUF4dSpU1JHokrm8OHDCAoK\nQpcuXWBhYYG4uDisXbsWTk5OUkcjIiIieiyWPSIAxsbG+Ouvv9Rn+I4fPy51JKoEkpKSEBoainbt\n2qGoqAh79uxBdHQ0vLy8pI5GRERE9FQse0T/9eAePi8vLwQGBmLXrl1SRyKJZGZmYuzYsXB3d0dK\nSgr++OMPHDx4EB06dJA6GhEREdEz4+JPRP9gbGyMzZs3Y9iwYXjttdewatUq9O/fX+pYLyQ1NRV3\n796t8Ll79+7h6tWrD525tLW1Rb169XQZr1K6fPkyvvnmGyxZsgRNmzbFmjVr8Prrr0Mmk0kdjYiI\niOi5yYQQQuoQRJWNEAITJ07EggULsGjRIowZM0bqSM9t6tSpCA8Pf6Z9jx8/XqMvTczOzsY333yD\nBQsWoG7duvjss88wcuRILoZOREREVVkyyx7RE8yePRuffvoppk6diunTp0sd57kkJyejRYsWT93P\n3t4eaWlpOkhU+RQUFGDhwoWYNWsWlEolPv74Y0yYMAGGhoZSRyMiIiJ6Wcl825roCcLCwmBqaorx\n48fj7t27+Pbbb6vMJX0uLi5o2bIlzpw5g8e9p6Ovr4/hw4frNlglUFJSgoiICEydOhUFBQUYN24c\nJk+eDDMzM6mjEREREWkMJ2gheopx48Zh9erV+PHHHzFgwADcu3dP6kjPbOjQoVAoFI/dXlJSggED\nBugwkbRUKhUiIyPRvHlzfPjhhxgwYADS0tIwa9YsFj0iIiKqdngZJ9EzOnjwIHr37g0HBwds2rQJ\nVlZWUkd6qkuXLqFJkyaPPLMnk8ng7u6O+Ph4CZLplhAC69atw2effYb09HSMGDEC06ZNg7W1tdTR\niIiIiLQlmWf2iJ6Rn58fDh06hJycHLRt2xbJyclSR3oqGxsbtGnTBnL5w7/qCoUCw4YNkyCVbsXE\nxMDb2xsDBw6Eu7s7zpw5gyVLlrDoERERUbXHskf0HBwdHREbGwsbGxv4+vpi+/btUkd6qiFDhjzy\nPsPy8vIqu6zEszh06BACAwPRpUsXWFpa4vjx41i7di0cHR2ljkZERESkEyx7RM/J0tISf//9N0JC\nQhASEoKffvpJ6khP9KhCJ5fL4e/vj0aNGkmQSLtOnz6N0NBQtGvXDqWlpdi3bx+io6Ph4eEhdTQi\nIiIinWLZI3oBBgYG+PXXXzFlyhS8++67GDt2LEpLSx+7f0FBgQ7TVVS3bl0EBQVVmKhFJpNh6NCh\nkmXShrNnz2Lo0KFwd3dHZmYmoqOjceDAAbRv317qaERERESSYNkjekEymQzTp0/Hb7/9hlWrVqFL\nly64efPmQ/uVlJSgU6dO2LdvnwQp7xs8eHCFSVpkMhn69u0rWR5NysrKwtixY+Hq6oq4uDj8/vvv\nOHz4MDp37ix1NCIiIiJJsewRvaSBAwfi4MGDyMzMRNu2bZGYmFhh+yeffIKjR49iyJAhkp3h69u3\nL/T19QEASqUSr776KiwtLSXJoim3bt3CpEmT4OTkhKioKPz4449ITExE//79q8xaiERERETaxLJH\npAEeHh44duwYmjRpgnbt2mHdunUAgI0bN+KHH34AAFy5cgWffPKJJPlMTEwQHBwMhUKB8vJyvPnm\nm5LkeJKioqJn2i8/Px+zZ8+Gg4MDfvnlF0yfPh3nzp3DmDFjnrimIBEREVFNw3X2iDSotLQUH374\nIRYtWoRPPvkEP/30E/Lz86FSqQDcv3xy+/bt6Natm86zbdy4EX369IGhoSFu3boFExMTnWd4nF9/\n/RWbNm1Sl+RHKSkpQUREBD7//HOUlZXh/fffx8SJE1GrVi0dJiUiIiKqMpKVUicgqk709PSwcOFC\ntGrVCnPnzsW9e/fURQ+4X/ZGjhyJ5ORkmJmZaTVLfn4+srKykJOTg/z8fMjlchgZGaF169Y4dOgQ\nAKB27dqoX78+rKys1Jd56tpff/2FESNGQKVS4dixY/Dx8amwvbS0FMuXL8eMGTOQm5uL9957D5Mm\nTYKFhYUkeYmIiIiqCp7ZI9KCiRMn4vvvv0d5eflD2/T09DB06FD8/PPPGhkrPT0dx44dw+nTp3Hm\n9GmcOXkSF69cQcEzXhb5gFXt2rCxsYFb69Zo0aIFWrVqhTZt2mi1lO7evRvdunVDeXk55HI52rdv\nj127dgEAVCoV/vzzT0yZMgUZGRkYMWIEpk+fjoYNG2otDxEREVE1ksyyR6Rh27ZtQ3BwMJ70qyWT\nybB161a89tprz338W7duYcuWLdi1cyf2REfj0vXrUMpkcNDXh1tpKVxUKtgBaACgMQBLACYAlADO\nAnADUPjfY+UAuPbfRxaAdACnlUqclstxtaQECrkcHi1bIqBzZ7z66qsIDAyEnp7ec2d+lISEBLRv\n3x6FhYUVzn7u3LkTKpUK//d//4eTJ0+iX79++Prrr+Hg4KCRcYmIiIhqCJY9Ik26dOkS3NzckJeX\nV6HA/JtcLke9evWQkpLyTJcj5uTkYPXq1fjzjz+w7+BB6Mlk8JfJEFBWho4AfAAYauy7uO8GgP0A\n9gHYo6+PUyUlqF2rFoJ79cLAQYPw6quvQi5/sTmezp07h7Zt2yI3N7fC2U+FQgFzc3PcuXMH/fr1\nQ3h4OJo3b66R74eIiIiohmHZI9KkAQMGYO3atdDX10dJSckT99XT08PAgQOxcuXKx+5z/PhxLPrx\nR/z+22+QlZUhWAj0VanQHYCphrM/TSaADQDW6+nhQGkpbK2tMfrddzF69GjUr1//mY9z6dIl+Pr6\n4ubNmygrK3vkPvPmzcOHH36omeBERERENRPLHpGmJSUlITIyEuvXr0diYiL09PRQVlb22Ms6169f\njz59+lT43OHDh/HVjBnYumMHmimVGF5aitG4f0lmZXAewM8AlunpIQ/AsBEjMG3aNFhbWz/x627e\nvIk2bdrg0qVLKC0tfeQ+CoUCjo6OOHPmzAufOSQiIiIilj0irUpPT8dff/2FNWvW4OjRo+ry8uDS\nRZlMhjp16uDs2bOwtLREWloaPnj/fWzZvh1+SiWmlZWhi5TfwFPcA7AEwDd6esiVy/FxWBgmTZoE\nIyOjh/bNzc2Fv78/zp49+9ii94BcLsfKlSsr5XqARERERFUEyx6Rrly9ehWbNm3Cn3/+iT179kCl\nUkEmk6G8vBz9+/eHq6srZn31FeyFwHelpZW65P3bPQCLAMxQKFCvYUP8sGQJunfvrt5eWFiITp06\n4cSJE08tesD9EmxjY4Pz589rbEIYIiIiohqGZY9ICrm5udi2bRvWr1+PLVu2oKioCAZyOcJVKnwA\noKrWmysAPpLL8YdKhXffeQffzpsHuVyOHj16YM+ePY+9R0+pVEIul6vvc5TL5WjcuDF++eUXBAUF\n6fA7ICIiIqo2WPaIpBQZGYm3hg9H/ZISdC0rw3cADKQOpQFrAYxRKmHr5ITGdnbYvn07ZDIZlEpl\nhfsXLS0t4ejoiFatWsHJyQnOzs5wdnaGo6OjZIu8ExEREVUTLHtEUvnuu+/w8cSJeBfAXCGqRcn7\npwsAfOVy3BYCDg4O8PHxQbNmzeDs7KwudtpcsJ2IiIiohmPZI5LC5MmTMXvWLMwRAh9JHUZLinD/\nss53FQrE6utjS1QUAgICpI5FREREVFOw7BHp2pw5czApLAwRQmCI1GF0oBTAG3I5oo2MsPfgQbi7\nu0sdiYiIiKgmYNkj0qU1a9bgzTffxHwhMF7qMDpUDKCHUokzFhY4lpCARo0aSR2JiIiIqLpj2SPS\nlbS0NHi6uWFkURHm18BfuzwAPkolGrVrh+jdu7lgOhEREZF2JfPVFpEOqFQqvBkaCseyMsyugUUP\nAGoBWFNWhoMHDmDu3LlSxyEiIiKq9nhmj0gHIiIiMPqtt5CgUqGl1GEkNhPA10ZGSL1wAQ0aNJA6\nDhEREVF1xcs4ibStsLAQzk2bIuTmTSzirxvuAWimp4fuw4fjp6VLpY5DREREVF3xMk4ibfv9999x\n69YtTNNA0fsZQD0Assc81j1iPxsAhf84RgyAJv/d5gIg/THH1QPQEEBPAH++dPL/MQLweWkpIiIi\ncPv2bQ0emYiIiIj+iWWPSMt+WboUfQBYaeBYowBE//fjDQAEgDIA+QDmPWK/RgCyAMz6x7bOADIB\nuANIBtD0Mce9BWD1fz9+HcBoDeR/4A0ABkJg9erVGjwqEREREf0Tyx6RFl26dAmxR49imEqltTEU\nAEwAeDxiW2cAAwDMAZDxnMc1B9AJwBbcL4M/A9j0wikrMgHQv7wca1au1NARiYiIiOjfWPaItOjA\ngQNQymQI0MFYgbh/Bu7ffsT94vbxSxx7Nu5f1qnJahYkBI4nJKCwsPDpOxMRERHRc2PZI9Kiw4cP\nw0OphLGWxxn+hG11ACzF/fvudr/g8S0BuAFIfMGvfxQ/AKXl5YiLi9PgUYmIiIjoAZY9Ii3KuHAB\nzUpKtHLsPvjfZCoxT9k3BMBQABMAlL/geLVxf2F0TbEFYCiXIzMzU4NHJSIiIqIHWPaItOj29euo\no6VjP5hIReD+vXlPswDAHQBLXnC8u7h/Oagm1VEqcevWLQ0flYiIiIgAlj0irSosKICRDsaJeIZ9\nzHF/kpVpuF/6nkcxgDN49CQwL8NEJkNBQYGGj0pEREREAMsekVbVrlPnuYvVyxj1lO3dAPQFMPU5\nj7sSQAGAYS8S6gmyVSpYWlpq+KhEREREBABKqQMQVWd1GzTATZkM0MCC6k+iwv376eKfYd9vAXgC\n0H/M9mLcv69PDuAagD8ATAIwFsBrL530f8oA3CkrQ926dTV4VCIiIiJ6gGf2iLSopasr4vX0NHa8\nXwB0/e/H/5ygRQHAAkDOv/ZbDaANgNJ/HMMUwH9w/16/B3797/EMAQzG/XeBFABaAvjrv8f7SWPf\nxX0nAZQLAVdXVw0fmYiIiIgAQCaElk85ENVgMTEx6NKlC64AaCh1mEpmIYCptWrhVk4O5HK+70RE\nRESkYcl8hUWkRW3atIGxgQE2SR2kEtqoVCKwUycWPSIiIiIt4assIi0yNTVFv/798YsGL+WsDtIB\n7C4vx4hRT5tShoiIiIheFMsekZaNGj0ax0pLcUDqIJXIfAAN69XDq6++KnUUIiIiomqLZY9IywIC\nAtA5MBAf6+mBN8jeP6u3RC7H5198AaWSEwITERERaQsnaCHSgRMnTsDH2xvLhcBQqcNILEShQKqd\nHRJTUlj2iIiIiLSHE7QQ6YKXlxfGjx+PcQoFzkkdRkI/AtiqUmHRf/7DokdERESkZTyzR6QjxcXF\naNO6NRRnz2JPWRlMpQ6kY0cABCoU+L/PPsP06dOljkNERERU3SWz7BHp0Pnz5+Hn6wvPu3fxV1kZ\n9KUOpCMpANorlWjTpQs2bt4MhUIhdSQiIiKi6o6XcRLpkqOjI7ZHR+OQvj4GKhQokjqQDiQD6Kqn\nB2cvL/yxbh2LHhEREZGOsOwR6ZiXlxe27tiBPcbG6KqnhztSB9KigwD8lUrYeHhgS1QUjI2NpY5E\nREREVGOw7BFJwN/fH/sPHUJGnTrw1dPDcakDacEiAJ0VCgS89hpi9u5F7dq1pY5EREREVKOw7BFJ\npGXLljhy4gRs/fzgJ5fjOwAqqUNpwG0A/RQKjJfL8X9TpmDdhg0wMjKSOhYRERFRjcOyRyShhg0b\n4u+dOzE1PBxhCgXa6OkhTupQL0gF4GcAzZRKHKlTBzE7d2LGjBm8R4+IiIhIIix7RBKTy+X/394d\nu0Z5BnAc/75v7j0h6CXVoMEzPUoDBc/bQohKNCRC261tnDIUikTokH8go39ANsGhECiZUpRoHLKc\nyp1TBDU2XIZLKaGXhhAKvStZ7t7rdairg6Z6yfX7Wd7x+b7jb3keZmdnebG2RvfwMCNBwI0w5Nd2\nh72FFWAkivg+DPl2ZoZSuczY2Fi7syRJkv7XHHvSIZHNZnlcLPLjwgKFgQE+C0NuhCGldoe9QRO4\nD1xMJPgSOD0+zvOXL5mbmyOVSrW5TpIkSY496RAJgoCpqSk2Njf5YX6ep5kMWf59o24B2G93IPAb\ncAv4JIr4Ogg4de0aq6urPFxZIZfLtTtPkiRJr/mounSItVot8vk8d27f5sHyMgngC2Cy2eRzoO8D\ndWwAy8DdKOJZo0Ffby/f3bzJ9PQ0g4ODH6hCkiRJb2HDsScdEXt7eywtLXFvcZFHT57QiGPOJ5Nc\nqde5CFwAzgPHDnjOH8DPwCugGIYUu7rYbTTo6+nhq+vX+WZykomJCZLJ5AFPkiRJ0nvk2JOOolqt\nRrFYpFAoUMjneb62Rj2O6QoCPk0m+bjZ5GwckwY+AnqAAOgFYuCv198qsAvsBAG/RxG/ADv1OgAn\nUykuXb7M1fFxRkdHGRoa8mZNSZKko8OxJ3WCOI4pl8usr69TKpWoVCrsVCpsb21RrVb5s1bj71aL\n6v4+XWFIqrubKJHgxPHjnOnvp39ggPS5c2QyGXK5HNlslnQ63e7fkiRJ0rtz7EmSJElSB9rwNk5J\nkiRJ6kCOPUmSJEnqQI49SZIkSepACeCndkdIkiRJkv5T2/8AfqA4vUy9O8oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "pCdKD3l7-ua8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training a Dialogue Model"
      ]
    },
    {
      "metadata": {
        "id": "4D7R-FRO5dxz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7364
        },
        "outputId": "727adf2a-fa4b-4158-df94-30ad472f62f3"
      },
      "cell_type": "code",
      "source": [
        "from rasa_core.policies import FallbackPolicy, KerasPolicy, MemoizationPolicy\n",
        "from rasa_core.agent import Agent\n",
        "\n",
        "# this will catch predictions the model isn't very certain about\n",
        "# there is a threshold for the NLU predictions as well as the action predictions\n",
        "fallback = FallbackPolicy(fallback_action_name=\"utter_unclear\",\n",
        "                          core_threshold=0.2,\n",
        "                          nlu_threshold=0.1)\n",
        "\n",
        "agent = Agent('domain.yml', policies=[MemoizationPolicy(), KerasPolicy(), fallback])\n",
        "\n",
        "# loading our neatly defined training dialogues\n",
        "training_data = agent.load_data('stories.md')\n",
        "\n",
        "agent.train(\n",
        "    training_data,\n",
        "    validation_split=0.0,\n",
        "    epochs=200\n",
        ")\n",
        "\n",
        "agent.persist('models/dialogue')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "Processed Story Blocks: 100%|██████████| 7/7 [00:00<00:00, 172.36it/s, # trackers=1]\n",
            "Processed Story Blocks: 100%|██████████| 7/7 [00:00<00:00, 148.93it/s, # trackers=7]\n",
            "Processed Story Blocks: 100%|██████████| 7/7 [00:00<00:00, 122.49it/s, # trackers=15]\n",
            "Processed Story Blocks: 100%|██████████| 7/7 [00:00<00:00, 113.26it/s, # trackers=15]\n",
            "INFO:rasa_core.featurizers:Creating states and action examples from collected trackers (by MaxHistoryTrackerFeaturizer)...\n",
            "Processed trackers: 100%|██████████| 241/241 [00:13<00:00, 17.45it/s, # actions=271]\n",
            "INFO:rasa_core.featurizers:Created 271 action examples.\n",
            "Processed actions: 271it [00:00, 307.46it/s, # examples=271]\n",
            "INFO:rasa_core.policies.memoization:Memorized 271 unique action examples.\n",
            "INFO:rasa_core.featurizers:Creating states and action examples from collected trackers (by MaxHistoryTrackerFeaturizer)...\n",
            "Processed trackers: 100%|██████████| 241/241 [00:14<00:00, 16.52it/s, # actions=271]\n",
            "INFO:rasa_core.featurizers:Created 271 action examples.\n",
            "INFO:rasa_core.policies.keras_policy:Fitting model with 271 total samples and a validation split of 0.0\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "masking_1 (Masking)          (None, 5, 18)             0         \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 32)                6528      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 9)                 297       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 9)                 0         \n",
            "=================================================================\n",
            "Total params: 6,825\n",
            "Trainable params: 6,825\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/200\n",
            "271/271 [==============================] - 1s 2ms/step - loss: 2.1206 - acc: 0.3210\n",
            "Epoch 2/200\n",
            "271/271 [==============================] - 0s 193us/step - loss: 2.0129 - acc: 0.4613\n",
            "Epoch 3/200\n",
            "271/271 [==============================] - 0s 193us/step - loss: 1.9060 - acc: 0.4649\n",
            "Epoch 4/200\n",
            "271/271 [==============================] - 0s 190us/step - loss: 1.8404 - acc: 0.4649\n",
            "Epoch 5/200\n",
            "271/271 [==============================] - 0s 186us/step - loss: 1.7740 - acc: 0.4649\n",
            "Epoch 6/200\n",
            "271/271 [==============================] - 0s 187us/step - loss: 1.7249 - acc: 0.4649\n",
            "Epoch 7/200\n",
            "271/271 [==============================] - 0s 196us/step - loss: 1.7062 - acc: 0.4649\n",
            "Epoch 8/200\n",
            "271/271 [==============================] - 0s 193us/step - loss: 1.6775 - acc: 0.4649\n",
            "Epoch 9/200\n",
            "271/271 [==============================] - 0s 196us/step - loss: 1.6483 - acc: 0.4649\n",
            "Epoch 10/200\n",
            "271/271 [==============================] - 0s 201us/step - loss: 1.6247 - acc: 0.4649\n",
            "Epoch 11/200\n",
            "271/271 [==============================] - 0s 192us/step - loss: 1.6016 - acc: 0.4649\n",
            "Epoch 12/200\n",
            "271/271 [==============================] - 0s 187us/step - loss: 1.5632 - acc: 0.4649\n",
            "Epoch 13/200\n",
            "271/271 [==============================] - 0s 189us/step - loss: 1.5444 - acc: 0.4649\n",
            "Epoch 14/200\n",
            "271/271 [==============================] - 0s 195us/step - loss: 1.5148 - acc: 0.4649\n",
            "Epoch 15/200\n",
            "271/271 [==============================] - 0s 184us/step - loss: 1.4964 - acc: 0.4649\n",
            "Epoch 16/200\n",
            "271/271 [==============================] - 0s 197us/step - loss: 1.4738 - acc: 0.4649\n",
            "Epoch 17/200\n",
            "271/271 [==============================] - 0s 179us/step - loss: 1.4407 - acc: 0.4649\n",
            "Epoch 18/200\n",
            "271/271 [==============================] - 0s 234us/step - loss: 1.4179 - acc: 0.4723\n",
            "Epoch 19/200\n",
            "271/271 [==============================] - 0s 220us/step - loss: 1.3824 - acc: 0.4649\n",
            "Epoch 20/200\n",
            "271/271 [==============================] - 0s 192us/step - loss: 1.3379 - acc: 0.4723\n",
            "Epoch 21/200\n",
            "271/271 [==============================] - 0s 195us/step - loss: 1.3163 - acc: 0.4723\n",
            "Epoch 22/200\n",
            "271/271 [==============================] - 0s 212us/step - loss: 1.2716 - acc: 0.4908\n",
            "Epoch 23/200\n",
            "271/271 [==============================] - 0s 200us/step - loss: 1.2556 - acc: 0.4945\n",
            "Epoch 24/200\n",
            "271/271 [==============================] - 0s 187us/step - loss: 1.2215 - acc: 0.5240\n",
            "Epoch 25/200\n",
            "271/271 [==============================] - 0s 191us/step - loss: 1.1890 - acc: 0.5277\n",
            "Epoch 26/200\n",
            "271/271 [==============================] - 0s 205us/step - loss: 1.1450 - acc: 0.5203\n",
            "Epoch 27/200\n",
            "271/271 [==============================] - 0s 192us/step - loss: 1.1280 - acc: 0.5314\n",
            "Epoch 28/200\n",
            "271/271 [==============================] - 0s 190us/step - loss: 1.0914 - acc: 0.5572\n",
            "Epoch 29/200\n",
            "271/271 [==============================] - 0s 197us/step - loss: 1.0757 - acc: 0.5609\n",
            "Epoch 30/200\n",
            "271/271 [==============================] - 0s 224us/step - loss: 1.0444 - acc: 0.5609\n",
            "Epoch 31/200\n",
            "271/271 [==============================] - 0s 193us/step - loss: 1.0061 - acc: 0.5683\n",
            "Epoch 32/200\n",
            "271/271 [==============================] - 0s 214us/step - loss: 0.9747 - acc: 0.6162\n",
            "Epoch 33/200\n",
            "271/271 [==============================] - 0s 191us/step - loss: 0.9587 - acc: 0.6531\n",
            "Epoch 34/200\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:rasa_core.policies.keras_policy:Done fitting keras policy model\n",
            "INFO:rasa_core.agent:Persisted model to '/content/models/dialogue'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "c4911z6y-5rD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Talk to your Bot"
      ]
    },
    {
      "metadata": {
        "id": "nE4coPam5hry",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c8ec135b-882b-4e9e-a955-f3e184177817"
      },
      "cell_type": "code",
      "source": [
        "#Starting the Bot\n",
        "\n",
        "from rasa_core.agent import Agent\n",
        "agent = Agent.load('models/dialogue', interpreter=model_directory)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:rasa_nlu.components:Added 'nlp_spacy' to component cache. Key 'nlp_spacy-en'.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "DDVLzhAT5yrP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "aee3fc83-df97-42b4-c7e0-c8929f76337c"
      },
      "cell_type": "code",
      "source": [
        "print(\"Your bot is ready to talk! Type your messages here or send 'stop'\")\n",
        "while True:\n",
        "    a = input()\n",
        "    if a == 'stop':\n",
        "        break\n",
        "    responses = agent.handle_message(a)\n",
        "    for response in responses:\n",
        "        print(response[\"text\"])\n",
        "        "
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your bot is ready to talk! Type your messages here or send 'stop'\n",
            "Hello\n",
            "Hey! How are you?\n",
            "I am unhappy\n",
            "To cheer you up, I can show you a cute picture of a dog, cat or a bird. Which one do you choose?\n",
            "yes. SHow me a cat\n",
            "Here is something to cheer you up: https://cdn.shibe.online/cats/692a286d46970ae1ca8ea15910de4ce000d8d219.jpg\n",
            "Did that help you?\n",
            "Yes\n",
            "Great carry on!\n",
            "Bye\n",
            "Bye\n",
            "Stop\n",
            "Hey! How are you?\n",
            "stop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "enJNkbvB54y1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}